Data-Driven Strategies for Accelerated Materials Design

Estimating fluid-solid interfacial free energies for wettabilities: A review of molecular simulation methods

Fluid-solid interfacial free energy (IFE) is a fundamental parameter influencing wetting behaviors, which play a crucial role across a broad range of industrial applications. Obtaining reliable data for fluid-solid IFE remains challenging with experimental and semi-empirical methods, and the applicability of first-principle theoretical methods is constrained by a lack of accessible computational tools. In recent years, a variety of molecular simulation methods have been developed for determining the fluid-solid IFE. This review provides a comprehensive summary and critical evaluation of these techniques. The developments, fundamental principles, and implementations of various simulation methods are presented from mechanical routes, such as the contact angle approach, the technique using Bakker's equation, and the Wilhelmy simulation method, as well as thermodynamic routes, including the cleaving wall method, the Frenkel-Ladd technique, and the test-volume/area methods. These approaches can be applied to compute various fluid-solid interfacial properties, including IFE, relative IFE, surface stress, and superficial tension, although these properties are often used without differentiation in the literature. Additionally, selected applications of these methods are reviewed to provide insight into the behavior of fluid-solid interfacial energies in diverse systems. We also illustrate two interpretations of the fluid-solid IFE based on the theory of Navascués and Berry and Bakker's equation. It is shown that the simulation methods developed from these two interpretations are identical. This review advocates for the broader adoption of molecular simulation methods in estimating fluid-solid IFE, which is essential for advancing our understanding of wetting behaviors in various chemical systems.

Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes

Transition-metal complexes serve as highly enantioselective homogeneous catalysts for various transformations, making them valuable in the pharmaceutical industry. Data-driven prediction models can accelerate high-throughput catalyst design but require computer-readable representations that account for conformational flexibility. This is typically achieved through high-level conformer searches, followed by DFT optimization of the transition-metal complexes. However, conformer selection remains reliant on human assumptions, with no cost-efficient and generalizable workflow available. To address this, we introduce an automated approach to correlate CREST(GFN2-xTB//GFN-FF)-generated conformer ensembles with their DFT-optimized counterparts for systematic conformer selection. We analyzed 24 precatalyst structures, performing CREST conformer searches, followed by full DFT optimization. Three filtering methods were evaluated: (i) geometric ligand descriptors, (ii) PCA-based selection, and (iii) DBSCAN clustering using RMSD and energy. The proposed methods were validated on Rh-based catalysts featuring bisphosphine ligands, which are widely employed in hydrogenation reactions. To assess general applicability, both the precatalyst and its corresponding acrylate-bound complex were analyzed. Our results confirm that CREST overestimates ligand flexibility, and energy-based filtering is ineffective. PCA-based selection failed to distinguish conformers by DFT energy, while RMSD-based filtering improved selection but lacked tunability. DBSCAN clustering provided the most effective approach, eliminating redundancies while preserving key configurations. This method remained robust across data sets and is computationally efficient without requiring molecular descriptor calculations. These findings highlight the limitations of energy-based filtering and the advantages of structure-based approaches for conformer selection. While DBSCAN clustering is a practical solution, its parameters remain system-dependent. For high-accuracy applications, refined energy calculations may be necessary; however, DBSCAN-based clustering offers a computationally accessible strategy for rapid catalyst representations involving conformational flexibility.

Enhancing molecular property prediction with quantized GNN models

Efficient and reliable prediction of molecular properties, such as water solubility, hydration free energy, lipophilicity, and quantum mechanical properties, is essential for rational compound design in the chemical and pharmaceutical industries. While Graph Neural Networks (GNNs) have significantly advanced molecular property prediction tasks, their high memory footprint, computational demands, and inference latency are often overlooked. These challenges hinder the deployment of property prediction models on resource-constrained devices such as smartphones and IoT devices. Therefore, optimizing storage, reducing resource consumption, and improving inference speed are crucial. This paper presents a systematic approach to molecular networks by integrating GNN models with the DoReFa-Net quantization algorithm. The proposed method aims to enhance computational efficiency while maintaining predictive performance, enabling lightweight yet effective models suitable for molecular task. The study investigates the impact of different bitwidth quantization levels on model performance, using metrics such as RMSE and MAE. Results show that, for physical chemistry datasets, the effectiveness of quantization is highly dependent on the model architecture. Notably, the quantum mechanical dipole moment task maintains strong performance up to 8-bit precision, achieving similar or slightly better results. However, extreme quantization, particularly at 2-bit precision, severely degrades performance, highlighting the limitations of aggressive compression.

A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes

Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and the energy transition. Compared to drug discovery within the chemical space of organic molecules, TMCs pose further challenges, including the encoding of chemical bonds of higher complexity and the need to optimize multiple properties. In this work, we developed a generative model for the inverse design of transition metal ligands and complexes, based on the junction tree variational autoencoder (JT-VAE). After implementing a SMILES-based encoding of the metal-ligand bonds, the model was trained with the tmQMg-L ligand library, allowing for the generation of thousands of novel, highly diverse monodentate (κ1) and bidentate (κ2) ligands, including imines, phosphines, and carbenes. Further, the generated ligands were labeled with two target properties reflecting the stability and electron density of the associated homoleptic iridium TMCs: the HOMO-LUMO gap (ϵ) and the charge of the metal center (q Ir). This data was used to implement a conditional model that generated ligands from a prompt, with the single- or dual-objective of optimizing either or both the ϵ and q Ir properties and allowing for chemical interpretation based on the optimization trajectories. The optimizations also had an impact on other chemical properties, including ligand dissociation energies and oxidative addition barriers. A similar model was implemented to condition ligand generation by solubility and steric bulk.

Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel

Grid searching a large and high-dimensional chemical space with density functional theory (DFT) to discover new materials with desired properties is prohibitive due to the high computational cost. We propose an approach utilizing Bayesian optimization (BO) with an artificial neural network kernel to enable an efficient and low-cost guided search on the chemical space, avoiding costly brute-force grid search. This method leverages the BO algorithm, where the kernel neural network trained on a limited number of DFT results determines the most promising regions of the chemical space to explore in subsequent iterations. This approach aims to discover new materials with target properties while minimizing the number of DFT calculations required. To demonstrate the effectiveness of this method, we investigated 63 doped graphene quantum dots (GQDs) with sizes ranging from 1 to 2 nm to find the structure with the highest light absorption. Using time-dependent DFT (TDDFT) only 12 times, we achieved a significant reduction in computational cost, approximately 20% of what would be required for a full grid search. Considering that TDDFT calculations for a single GQD require about half a day of wall time on high-performance computing nodes, this reduction is substantial. Our approach can be generalized to the discovery of new drugs, chemicals, crystals, and alloys in high-dimensional and large chemical spaces, offering a scalable solution enabled by the neural network kernel.

Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?

The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance of quantifying molecular similarity. Molecular similarity, commonly assessed as the distance between molecular fingerprints, is integral to applications such as database curation, diversity analysis, and property prediction. AI tools frequently rely on these similarity measures to cluster molecules under the assumption that structurally similar molecules exhibit similar properties. However, this assumption is not universally valid, particularly for continuous properties like electronic structure properties. Despite the prevalence of fingerprint-based similarity measures, their evaluation has largely depended on biological activity data sets and qualitative metrics, limiting their relevance for nonbiological domains. To address this gap, we propose a framework to evaluate the correlation between molecular similarity measures and molecular properties. Our approach builds on the concept of neighborhood behavior and incorporates kernel density estimation (KDE) analysis to quantify how well similarity measures capture property relationships. Using a data set of over 350 million molecule pairs with electronic structure, redox, and optical properties, we systematically evaluate the correlation between several molecular fingerprint generators, distance functions, and these properties. Both the curated data set and the evaluation framework are publicly available.

Data selection strategies for minimizing measurement time in materials characterization

Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time-consuming and, eventually, not efficient. The present study highlights that specific selection strategies, taking into account the prevailing microstructure of the alloy in focus, can decrease counting times in X-ray energy dispersive diffraction experiments without any detrimental effect on data quality for the subsequent analysis. All relevant data, including the code, are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also in large-scale facilities.

Materials Advances in Devices for Heart Disease Interventions

Heart disease encompasses a range of conditions that affect the heart, including coronary artery disease, arrhythmias, congenital heart defects, heart valve disease, and conditions that affect the heart muscle. Intervention strategies can be categorized according to when they are administered and include: 1) Monitoring cardiac function using sensor technology to inform diagnosis and treatment, 2) Managing symptoms by restoring cardiac output, electrophysiology, and hemodynamics, and often serving as bridge-to-recovery or bridge-to-transplantation strategies, and 3) Repairing damaged tissue, including myocardium and heart valves, when management strategies are insufficient. Each intervention approach and technology require specific material properties to function optimally, relying on materials that support their action and interface with the body, with new technologies increasingly depending on advances in materials science and engineering. This review explores material properties and requirements driving innovation in advanced intervention strategies for heart disease and highlights key examples of recent progress in the field driven by advances in materials research.

Bayesian molecular optimization for accelerating reverse intersystem crossing

Spin conversion in molecular excited states is crucial for the development of next-generation optoelectronic devices. However, optimizing molecular structures for rapid spin conversion has relied on time-consuming experimental trial-and-error, which limits the elucidation of the structure-property relationships. Here, we report a Bayesian molecular optimization approach that accelerates virtual screening for rapid triplet-to-singlet reverse intersystem crossing (RISC). One of the molecules identified through this virtual screening exhibits a fast RISC rate constant of 1.3 × 108 s-1 and a high external electroluminescence quantum efficiency of 25.7%, which remains as high as 22.8% even at a practical luminance of 5000 cd m-2 in organic light-emitting diodes. Post-hoc analysis of the trained machine learning model reveals the impact of molecular structural features on spin conversion, paving the way for informed and precise materials development.

Uncertainty quantification with graph neural networks for efficient molecular design

Optimizing molecular design across expansive chemical spaces presents unique challenges, especially in maintaining predictive accuracy under domain shifts. This study integrates uncertainty quantification (UQ), directed message passing neural networks (D-MPNNs), and genetic algorithms (GAs) to address these challenges. We systematically evaluate whether UQ-enhanced D-MPNNs can effectively optimize broad, open-ended chemical spaces and identify the most effective implementation strategies. Using benchmarks from the Tartarus and GuacaMol platforms, our results show that UQ integration via probabilistic improvement optimization (PIO) enhances optimization success in most cases, supporting more reliable exploration of chemically diverse regions. In multi-objective tasks, PIO proves especially advantageous, balancing competing objectives and outperforming uncertainty-agnostic approaches. This work provides practical guidelines for integrating UQ in computational-aided molecular design (CAMD).

Accelerated design of solid bio-based foams for plastics substitutes

Biobased substitutes for plastics are a future necessity. However, the design of substitute materials with similar or improved properties is a known challenge. Here we show an example case of optimizing the mechanical properties of a fully biobased methylcellulose-fiber composite material. We tackle the process-structure-property paradigm using Bayesian optimization with Gaussian process regression to map the processed material composition to the final mechanical properties of new bio-based solid foams. We exploited the fast-to-measure rheological properties of the liquid biofiber suspensions processed into foams to show how these collapse to an auxiliary sub-space of low dimensionality for design. The optimal compositions for methylcellulose-fiber foams shown here correspond to two distinct cases: high methylcellulose content for the formation of strong closed-cell foams, and high fiber contents with approximately equal amounts of methylcellulose for the formation of methylcellulose-bound fiber networks. The novel approach is transferable to other biobased foam compositions with different fibers and additives. This new approach allows the rational design of bio-based plastics replacements by encompassing desired final material properties, descriptors of materials processed, and knowledge of the process.

Machine learning-assisted design and prediction of materials for batteries based on alkali metals

Since the commercialization of lithium-ion batteries in the 1990s, batteries based on alkali metals have been promising candidates for energy storage. The performances of these batteries, in terms of cost-efficiency, energy density, safety, and cycle life need continuous improvement. Battery performances are highly dependent on electrode materials, yet the long experimental period, intensive labor, and high cost remain bottlenecks in the improvement of electrode materials. Machine learning (ML), which is being increasingly integrated into materials science, offers transformative potential by reducing the R&D period and cost. ML also demonstrates significant advantages in the performance prediction of various materials, and it can also help reveal the structure-performance relationship of materials. ML-assisted material design and performance prediction thus enable the innovation of advanced materials. Herein, implementation of ML for exploring alkali metal-based batteries is outlined, highlighting various ML algorithms as well as electrode reaction mechanisms.

Machine Learning in Polymer Research

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.

MOSAEC-DB: a comprehensive database of experimental metal-organic frameworks with verified chemical accuracy suitable for molecular simulations

Ongoing developments in computational databases seek to improve the accessibility and breadth of high-throughput screening and materials discovery efforts. Their reliance on experimental crystal structures necessitates significant processing prior to computation in order to resolve any crystallographic disorder or partial occupancies and remove any residual solvent molecules in the case of activated porous materials. Contemporary investigations revealed that deficiencies in the experimental characterization and computational preprocessing methods generated considerable occurrence of structural errors in metal-organic framework (MOF) databases. The MOSAEC MOF database (MOSAEC-DB) tackles these structural reliability concerns through utilization of innovative preprocessing and error analysis protocols applying the concepts of oxidation state and formal charge to exclude erroneous crystal structures. Comprising more than 124k crystal structures, this work maintains the largest and most accurate dataset of experimental MOFs ready for immediate deployment in molecular simulations. The databases' comparative diversity is demonstrated through its enhanced coverage of the periodic table, expansive quantity of structures, and balance of chemical properties relative to existing MOF databases. Chemical and geometric descriptors, as well as DFT electrostatic potential-fitted charges, are included to facilitate subsequent atomistic simulation and machine-learning (ML) studies. Curated subsets-sampled according to their chemical properties and structural uniqueness-are also provided to further enable ML studies in recognition of the strict demand for duplicate structure elimination and dataset diversity in such applications.

Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach

The two-dimensionality (2D) of charge transport significantly affects charge carrier mobility in organic semiconductors, making it a key target for materials discovery and design. Traditional quantum-chemical methods for calculating 2D are resource-intensive, especially for large-scale screening, as they require computing charge transfer integrals for all unique pairs of interacting molecules. We explore the potential of machine learning models to predict whether this parameter will fall within a desirable range without performing any quantum-chemical calculations. Using a large database of molecular semiconductors with known 2D values, we evaluate various machine-learning models using chemical and geometrical descriptors. Our findings demonstrate that the LightGBM outperforms others, achieving 95% accuracy in predictions. These results are expected to facilitate the systematic identification of high-mobility molecular semiconductors.

Functionalization of Clar's Goblet Diradical with Heteroatoms: Tuning the Excited-State Energies to Promote Triplet-to-Singlet Conversion

The ground-state spin multiplicity as well as the energy difference between the lowest-energy spin-singlet (S1) and spin-triplet (T1) excited states of topologically frustrated organic (diradical) molecules can be tuned by doping with a pair of heteroatoms (N or B atoms). We have thus systematically studied here a set of Clar's Goblet derivatives upon a controlled substitution at different C sites, to alter the electronic structure of the molecules and disclose the positions at which: (i) the ground-state multiplicity becomes a closed-shell singlet and (ii) the energy difference between S1 and T1 is considerably small (i.e., below 0.1-0.2 eV to induce a triplet exciton recovery upon thermal effects). This electronic structure outcome is driven by strong correlation effects; therefore, we have here applied a variety of single-reference [TD-DFT, CIS(D), SCS-CC2] and multireference [CASSCF, NEVPT2, RAS-srDFT] methods. For TD-DFT, we have covered global hybrid (PBE0, M06-2X), range-separated hybrid (ωB97X), and double-hybrid (PBE-QIDH, SOS1-PBE-QIDH, and PBE0-2) functionals to ascertain whether the results were highly dependent on the functional choice. Overall, we found that the heterosubstitution strategy could largely modify the electronic and optical properties of the pristine diradical system, with these organic forms thus constituting a new set of compounds with further optoelectronic applications.

A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes

The ability to precisely design colloidal nanocrystals (NCs) has far-reaching implications in optoelectronics, catalysis, biomedicine, and beyond. Achieving such control is generally based on a trial-and-error approach. Data-driven synthesis holds promise to advance both discovery and mechanistic knowledge. Herein, we contribute to advancing the current state of the art in the chemical synthesis of colloidal NCs by proposing a machine-learning toolbox that operates in a low-data regime, yet comprehensive of the most typical parameters relevant for colloidal NC synthesis. The developed toolbox predicts the NC shape given the reaction conditions and proposes reaction conditions given a target NC shape using Cu NCs as the model system. By classifying NC shapes on a continuous energy scale, we synthesize an unreported shape, which is the Cu rhombic dodecahedron. This holistic approach integrates data-driven and computational tools with materials chemistry. Such development is promising to greatly accelerate materials discovery and mechanistic understanding, thus advancing the field of tailored materials with atomic-scale precision tunability.

3D Printing of Porous Ceramics for Enhanced Thermal Insulation Properties

Porous thermal insulating ceramics play a pivotal role in both industrial processes and daily life by offering effective insulation solutions that reduce energy consumption, enhance building comfort, and contribute to the sustainability of industrial production. This review offers a comprehensive examination of porous thermal insulating ceramics produced by 3D printing, providing an in-depth analysis of various 3D printing techniques and materials used to produce porous ceramics, detailing the fabrication processes, advantages, and limitations of these methods. Recent advances in 3D printed porous thermal insulating ceramics are thoroughly examined, with a particular focus on pore structure design and optimization strategies for high-performance thermal insulation. This review also addresses the challenges and barriers to widespread adoption while highlighting future research directions and emerging trends poised to drive innovation. By showcasing the transformative potential of 3D printing in revolutionizing traditional porous ceramics manufacturing methods and enhancing thermal insulation performance, this review underscores the critical role of 3D printed porous ceramics in advancing thermal insulation technology.

Climate friendly MOFs synthesis for drug delivery systems by integrating AI, intelligent manufacturing, and quantum solutions in industry 6.0 sustainable approach

Since the Industrial Revolution, ecological damage, ecosystem disruption, and climate change acceleration have frequently resulted from human advancement at the price of the environment. Due to the rise in illnesses, Industry 6.0 calls for a renewed dedication to sustainability with latest technologies. Focused research and creative solutions are needed to achieve the UN Sustainable Development Goals (SDGs), especially 3, 9, 13, 14, 15, 17. A promising sustainable technology for enhancing healthcare while reducing environmental effect is Metal Organic Frameworks (MOFs). MOFs are perfect for drug administration because of their high surface areas, adjustable pore sizes, and remarkable drug-loading capacities. They are created by combining advanced artificial intelligence, intelligent manufacturing, and quantum computing. Researchers can create MOFs with functional groups or ligands that bind selectively to target cells or tissues, minimizing off-target effects, thanks to the distinct benefits that families like MIL, HKUST, UiO, and ZIF etc. offer for targeted drug delivery. Combining MOFs with other nanomaterials results in multipurpose systems that can handle challenging biomedical issues. Despite its promise, there are still issues with MOFs' possible toxicity and long-term stability in physiological settings. To advance their medicinal applications, these problems must be resolved. Researchers can increase the usefulness of MOFs in medicine by critically analysing these limitations and putting up creative alternatives. The creation of MOFs especially with advanced technologies (additive manufacturing etc.) for drug delivery is a prime example of how scientific advancement and environmental stewardship may coexist to provide healthcare solutions that are advantageous to both people and the environment.

Qcforever2: Advanced Automation of Quantum Chemistry Computations

QCforever is a wrapper designed to automatically and simultaneously calculate various physical quantities using quantum chemical (QC) calculation software for blackbox optimization in chemical space. We have updated it to QCforever2 to search the conformation and optimize density functional parameters for a more accurate and reliable evaluation of an input molecule. In blackbox optimization, QCforever2 can work as compactly arranged surrogate models for costly chemical experiments. QCforever2 is the future of QC calculations and would be a good companion for chemical laboratories, providing more reliable search and exploitation in the chemical space.

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.

Neural Ordinary Differential Equations for Forecasting and Accelerating Photon Correlation Spectroscopy

Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments. Photon correlation Fourier spectroscopy (PCFS) is one such technique that measures time-resolved single-emitter line shapes and offers additional spectral information over Hong-Ou-Mandel two-photon interference but requires long experimental acquisition times. Here, we demonstrate a neural ordinary differential equation model, g2NODE, that can forecast a complete and noise-free interferometry experiment from a small subset of noisy correlation functions. We demonstrate this for simulated and experimental data, where g2NODE utilizes 10-20 noisy measured photon correlation functions to create entire denoised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from hours to minutes. Our work presents a new deep learning approach to greatly accelerate the use of photon correlation spectroscopy as an experimental characterization tool for novel quantum emitter materials.

Large property models: a new generative machine-learning formulation for molecules

Generative models for the inverse design of molecules with particular properties have been heavily hyped, but have yet to demonstrate significant gains over machine-learning-augmented expert intuition. A major challenge of such models is their limited accuracy in predicting molecules with targeted properties in the data-scarce regime, which is the regime typical of the prized outliers that it is hoped inverse models will discover. For example, activity data for a drug target or stability data for a material may only number in the tens to hundreds of samples, which is insufficient to learn an accurate and reasonably general property-to-structure inverse mapping from scratch. We've hypothesized that the property-to-structure mapping becomes unique when a sufficient number of properties are supplied to the models during training. This hypothesis has several important corollaries if true. It would imply that data-scarce properties can be completely determined using a set of more accessible molecular properties. It would also imply that a generative model trained on multiple properties would exhibit an accuracy phase transition after achieving a sufficient size-a process analogous to what has been observed in the context of large language models. To interrogate these behaviors, we have built the first transformers trained on the property-to-molecular-graph task, which we dub "large property models" (LPMs). A key ingredient is supplementing these models during training with relatively basic but abundant chemical property data. The motivation for the large-property-model paradigm, the model architectures, and case studies are presented here.

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning

Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expressive both globally (molecular topology) and locally (atom and bond properties). Graph kernels are used to transform molecular graphs into fixed-length vectors, which, based on their capacity of measuring similarity, can be used as fingerprints for machine learning (ML). To date, graph kernels have mostly focused on the atomic nodes of the graph. In this work, we developed a graph kernel based on atom-atom, bond-bond, and bond-atom (AABBA) autocorrelations. The resulting vector representations were tested on regression ML tasks on a data set of transition metal complexes; a benchmark motivated by the higher complexity of these compounds relative to organic molecules. In particular, we tested different flavors of the AABBA kernel in the prediction of the energy barriers and bond distances of the Vaska's complex data set (Friederich et al., Chem. Sci., 2020, 11, 4584). For a variety of ML models, including neural networks, gradient boosting machines, and Gaussian processes, we showed that AABBA outperforms the baseline including only atom-atom autocorrelations. Dimensionality reduction studies also showed that the bond-bond and bond-atom autocorrelations yield many of the most relevant features. We believe that the AABBA graph kernel can accelerate the exploration of large chemical spaces and inspire novel molecular representations in which both atomic and bond properties play an important role.

A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor

There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young's modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

De novo Design of Biocompatible Nanomaterials Using Quasi-SMILES and Recurrent Neural Networks

Screening nanomaterials (NMs) with desired properties from the extensive chemical space presents significant challenges. The potential toxicity of NMs further limits their applications in biological systems. Traditional methods struggle with these complexities, but generative models offer a possible solution to producing new molecules without prior knowledge. However, converting complex 3D nanostructures into computer-readable formats remains a critical prerequisite. To overcome these challenges, we proposed an innovative deep-learning framework for the de novo design of biocompatible NMs. This framework comprises two predictive models and a generative model, utilizing a Quasi-SMILES representation to encode three-dimensional structural information on NMs. Our generative model successfully created 289 new NMs not previously seen in the training set. The predictive models identified a particularly promising NM characterized by high cellular uptake and low toxicity. This NM was successfully synthesized, and its predicted properties were experimentally validated. Our approach advances the application of artificial intelligence in NM design and provides a practical solution for balancing functionality and toxicity in NMs.

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties. Machine learning techniques are utilized to efficiently predict various material properties, construct phase diagrams, and search for materials satisfying target properties. These computational approaches have elucidated the mechanisms behind material functionalities and explored promising materials in combination with synthesis, characterization, and device fabrication. Examples include the development of ternary nitride semiconductors for potential optoelectronic and photovoltaic applications, the exploration of phosphide semiconductors and the optimization of heterointerfaces toward the improvement of phosphide-based photovoltaic cells, and the discovery of ferroelectricity in layered perovskite oxides and the theoretical understanding of its origin, all of which demonstrate the effectiveness of our computer-aided materials research.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures

Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials and applications.

Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning-Regulated Solvent-Precursor Interactions for Superior Zinc Storage

Carbon superstructures with exquisite morphologies and functionalities show appealing prospects in energy realms, but the systematic tailoring of their microstructures remains a perplexing topic. Here, hydrangea-shaped heterodiatomic carbon superstructures (CHS) are designed using a solution phase manufacturing route, wherein machine learning workflow is applied to screen precursor-matched solvent for optimizing solvent-precursor interaction. Based on the established solubility parameter model and molecular growth kinetics simulation, ethanol as the optimal solvent stimulates thermodynamic solubilization and growth of polymeric intermediates to evoke CHS. Featured with surface-active motifs and consecutive charge transfer paths, CHS allows high accessibility of zincophilic sites and fast ion migration with low energy barriers. A anion-cation hybrid charge storage mechanism of CHS cathode is disclosed, which entails physical alternate uptake of Zn2+/CF3SO3 - ions at electroactive sites and chemical bipedal redox of Zn2+ ions with carbonyl/pyridine motifs. Such a beneficial electrochemistry contributes to all-round improvement in Zn-ion storage, involving excellent capacities (231 mAh g-1 at 0.5 A g-1; 132 mAh g-1 at 50 A g-1), high energy density (152 Wh kg-1), and long-lasting cyclability (100 000 cycles). This work expands the design versatilities of superstructure materials and will accelerate experimental procedures during carbon manufacturing through machine learning in the future.

Data-Driven Materials Research and Development for Functional Coatings

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.

Topological Characterization of Metal-Organic Frameworks: A Perspective

Metal-organic frameworks (MOFs) began to emerge over two decades ago, resulting in the deposition of 120 000 MOF-like structures (and counting) into the Cambridge Structural Database (CSD). Topological analysis is a critical step toward understanding periodic MOF materials, offering insight into the design and synthesis of these crystals via the simplification of connectivity imposed on the complete chemical structure. While some of the most prevalent topologies, such as face-centered cubic (fcu), square lattice (sql), and diamond (dia), are simple and can be easily assigned to structures, MOFs that are built from complex building blocks, with multiple nodes of different symmetry, result in difficult to characterize topological configurations. In these complex structures, representations can easily diverge where the definition of nodes and linkers are blurred, especially for cases where they are not immediately obvious in chemical terms. Currently, researchers have the option to use software such as ToposPro, MOFid, and CrystalNets to aid in the assignment of topology descriptors to new and existing MOFs. These software packages are readily available and are frequently used to simplify original MOF structures into their basic connectivity representations before algorithmically matching these condensed representations to a database of underlying mathematical nets. These approaches often require the use of in-built bond assignment algorithms alongside the simplification and matching rules. In this Perspective, we discuss the importance of topology within the field of MOFs, the methods and techniques implemented by these software packages, and their availability and limitations and review their uptake within the MOF community.

Data science-centric design, discovery, and evaluation of novel synthetically accessible polyimides with desired dielectric constants

Rapidly advancing computer technology has demonstrated great potential in recent years to assist in the generation and discovery of promising molecular structures. Herein, we present a data science-centric "Design-Discovery-Evaluation" scheme for exploring novel polyimides (PIs) with desired dielectric constants (ε). A virtual library of over 100 000 synthetically accessible PIs is created by extending existing PIs. Within the framework of quantitative structure-property relationship (QSPR), a model sufficient to predict ε at multiple frequencies is developed with an R 2 of 0.9768, allowing further high-throughput screening of the prior structures with desired ε. Furthermore, the structural feature representation method of atomic adjacent group (AAG) is introduced, using which the reliability of high-throughput screening results is evaluated. This workflow identifies 9 novel PIs (ε >5 at 103 Hz and glass transition temperatures between 250 °C and 350 °C) with potential applications in high-temperature capacitive energy storage, and confirms these promising findings by high-fidelity molecular dynamics (MD) simulations.

Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites

The exploration of large chemical spaces in search of new thermoelectric materials requires the integration of experiments, theory, simulations, and data science. The development of high-throughput strategies that combine DFT calculations with machine learning has emerged as a powerful approach to discovering new materials. However, experimental validation is crucial to confirm the accuracy of these workflows. This validation becomes especially important in understanding the transport properties that govern the thermoelectric performance of materials since they are highly influenced by synthetic, processing, and operating conditions. In this work, we explore the thermal conductivity of Cu-based sulvanites by using a combination of theoretical and experimental methods. Previous discrepancies and significant variations in reported data for Cu3VS4 and Cu3VSe4 are explained using the Boltzmann Transport Equation for phonons and by synthesizing well-characterized defect-free samples. The use of machine learning approaches for extracting high-order force constants opens doors to charting the lattice thermal conductivity across the entire Cu-based sulvanite family-finding not only materials with κ l values below 2 W m-1 K-1 at moderate temperatures but also rationalizing their thermal transport properties based on chemical composition.

Automated prediction of ground state spin for transition metal complexes

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.

Chemistries and materials for atmospheric water harvesting

Atmospheric water harvesting (AWH) is recognized as a crucial strategy to address the global challenge of water scarcity by tapping into the vast reserves of atmospheric moisture for potable water supply. Within this domain, sorbents lie in the core of AWH technologies as they possess broad adaptability across a wide spectrum of humidity levels, underpinned by the cyclic sorption and desorption processes of sorbents, necessitating a multi-scale viewpoint regarding the rational material and chemical selection and design. This Invited Review delves into the essential sorption mechanisms observed across various classes of sorbent systems, emphasizing the water-sorbent interactions and the progression of water networks. A special focus is placed on the insights derived from isotherm profiles, which elucidate sorbent structures and sorption dynamics. From these foundational principles, we derive material and chemical design guidelines and identify key tuning factors from a structural-functional perspective across multiple material systems, addressing their fundamental chemistries and unique attributes. The review further navigates through system-level design considerations to optimize water production efficiency. This review aims to equip researchers in the field of AWH with a thorough understanding of the water-sorbent interactions, material design principles, and system-level considerations essential for advancing this technology.

Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of an integrated platform; we conducted simultaneous optimizations of four different terminal alkynes and two reaction routes using an automation platform combined with a Bayesian optimization platform. Remarkably, we achieved a conversion rate of over 80% for all four substrates in 23 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to identify the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.

Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

EnTdecker - A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis

Due to the magnitude of chemical space, the discovery of novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies to identify compounds that successfully undergo EnT-mediated reactions are limited by their time and cost efficiency. To accelerate the discovery process in EnT catalysis, we herein present the EnTdecker platform, which facilitates the large-scale virtual screening of potential substrates using machine-learning (ML) based predictions of their excited state properties. To achieve this, a data set is created containing more than 34,000 molecules aiming to cover a vast fraction of synthetically relevant compound space for EnT catalysis. Using this data predictive models are trained, and their aptitude for an in-lab application is demonstrated by rediscovering successful substrates from literature as well as experimental validation through luminescence-based screening. By reducing the computational effort needed to obtain excited state properties, the EnTdecker platform represents a tool to efficiently guide substrate selection and increase the experimental success rate for EnT catalysis. Moreover, through an easy-to-use web application, EnTdecker is made publicly accessible under entdecker.uni-muenster.de.

Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies

Chemistry is experiencing a paradigm shift in the way it interacts with data. So-called "big data" are collected and used at unprecedented scales with the idea that algorithms can be designed to aid in chemical discovery. As data-enabled practices become ever more ubiquitous, chemists must consider the organization and curation of their data, especially as it is presented to both humans and increasingly intelligent algorithms. One of the most promising organizational schemes for big data is a construct termed an ontology. In data science, ontologies are systems that represent relations among objects and properties in a domain of discourse. As chemistry encounters larger and larger data sets, the ontologies that support chemical research will likewise increase in complexity, and the future of chemistry will be shaped by the choices made in developing big data chemical ontologies. How such ontologies will work should therefore be a subject of significant attention in the chemical community. Now is the time for chemists to ask questions about ontology design and use: How should chemical data be organized? What can be reasonably expected from an organizational structure? Is a universal ontology tenable? As some of these questions may be new to chemists, we recommend an interdisciplinary approach that draws on the long history of philosophers of science asking questions about the organization of scientific concepts, constructs, models, and theories. This Perspective presents insights from these long-standing studies and initiates new conversations between chemists and philosophers.

Interoperable workflows by exchanging grid-based data between quantum-chemical program packages

Quantum-chemical subsystem and embedding methods require complex workflows that may involve multiple quantum-chemical program packages. Moreover, such workflows require the exchange of voluminous data that go beyond simple quantities, such as molecular structures and energies. Here, we describe our approach for addressing this interoperability challenge by exchanging electron densities and embedding potentials as grid-based data. We describe the approach that we have implemented to this end in a dedicated code, PyEmbed, currently part of a Python scripting framework. We discuss how it has facilitated the development of quantum-chemical subsystem and embedding methods and highlight several applications that have been enabled by PyEmbed, including wave-function theory (WFT) in density-functional theory (DFT) embedding schemes mixing non-relativistic and relativistic electronic structure methods, real-time time-dependent DFT-in-DFT approaches, the density-based many-body expansion, and workflows including real-space data analysis and visualization. Our approach demonstrates, in particular, the merits of exchanging (complex) grid-based data and, in general, the potential of modular software development in quantum chemistry, which hinges upon libraries that facilitate interoperability.

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder.

Directional multiobjective optimization of metal complexes at the billion-system scale

The discovery of transition metal complexes (TMCs) with optimal properties requires large ligand libraries and efficient multiobjective optimization algorithms. Here we provide the tmQMg-L library, containing 30k diverse and synthesizable ligands with robustly assigned charges and metal coordination modes. tmQMg-L enabled the generation of 1.37 million palladium TMCs, which were used to develop and benchmark the Pareto-Lighthouse multiobjective genetic algorithm (PL-MOGA). With fine control over aim and scope, this algorithm maximized both the polarizability and highest occupied molecular orbital-lowest unoccupied molecular orbital gap of the TMCs within selected regions of the Pareto front, without requiring prior knowledge on the objective limits. Instead of genetic operations on small ligand fragments, the PL-MOGA did whole-ligand mutation and crossover operations, which in chemical spaces containing billions of systems, yielded thousands of highly diverse TMCs in an interpretable manner.

Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network

The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.