To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks

Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.

Revealing operando surface defect-dependent electrocatalytic performance of Pt at the subparticle level

Understanding the operando defect-tuning performance of catalysts is critical to establish an accurate structure-activity relationship of a catalyst. Here, with the tool of single-molecule super-resolution fluorescence microscopy, by imaging intermediate CO formation/oxidation during the methanol oxidation reaction process on individual defective Pt nanotubes, we reveal that the fresh Pt ends with more defects are more active and anti-CO poisoning than fresh center areas with less defects, while such difference could be reversed after catalysis-induced step-by-step creation of more defects on the Pt surface. Further experimental results reveal an operando volcano relationship between the catalytic performance (activity and anti-CO ability) and the fine-tuned defect density. Systematic DFT calculations indicate that such an operando volcano relationship could be attributed to the defect-dependent transition state free energy and the accelerated surface reconstructing of defects or Pt-atom moving driven by the adsorption of the CO intermediate. These insights deepen our understanding to the operando defect-driven catalysis at single-molecule and subparticle level, which is able to help the design of highly efficient defect-based catalysts.

DFT Guided Design and Preparation of Quasi-Nanocrystalline Hf-La2O3 Cathode with Unprecedented Thermal Emission Performance

With the guidance of density functional theory (DFT), a high-performance hafnium (Hf) cathode for an air/water vapor plasma torch is designed and the concepts and principles for high performance are elucidated. A quasi-nanocrystalline hexagonal close-packed (HCP) Hf-La2O3 cathode based on these design principles is successfully fabricated via a powder metallurgy route. Under identical voltage and temperature conditions, the thermal emission current density of this quasi-nanocrystalline Hf-La2O3 cathode is ≈20 times greater than that of conventional Hf cathodes. Additionally, its cathodic lifespan is significantly extended. Quasi-nanocrystalline Hf-La2O3 products are manufactured into cathode devices with standard dimensions. This fabrication process is straightforward, requires minimal doped oxides, and is cost-effective. Consequently, the approach offers substantial performance enhancements over traditional Hf melting methods without incurring significantly additional costs.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Nanozymes with biomimetically designed properties for cancer treatment

Nanozymes, as a type of nanomaterials with enzymatic catalytic activity, have demonstrated tremendous potential in cancer treatment owing to their unique biomedical properties. However, the heterogeneity of tumors and the complex tumor microenvironment pose significant challenges to the in vivo catalytic efficacy of traditional nanozymes. Drawing inspiration from natural enzymes, scientists are now using biomimetic design to build nanozymes from the ground up. This approach aims to replicate the key characteristics of natural enzymes, including active structures, catalytic processes, and the ability to adapt to the tumor environment. This achieves selective optimization of nanozyme catalytic performance and therapeutic effects. This review takes a deep dive into the use of these biomimetically designed nanozymes in cancer treatment. It explores a range of biomimetic design strategies, from structural and process mimicry to advanced functional biomimicry. A significant focus is on tweaking the nanozyme structures to boost their catalytic performance, integrating them into complex enzyme networks similar to those in biological systems, and adjusting functions like altering tumor metabolism, reshaping the tumor environment, and enhancing drug delivery. The review also covers the applications of specially designed nanozymes in pan-cancer treatment, from catalytic therapy to improved traditional methods like chemotherapy, radiotherapy, and sonodynamic therapy, specifically analyzing the anti-tumor mechanisms of different therapeutic combination systems. Through rational design, these biomimetically designed nanozymes not only deepen the understanding of the regulatory mechanisms of nanozyme structure and performance but also adapt profoundly to tumor physiology, optimizing therapeutic effects and paving new pathways for innovative cancer treatment.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Multiscale Investigation of the Mechanism and Selectivity of CO2 Hydrogenation over Rh(111)

CO2 hydrogenation over Rh catalysts comprises multiple reaction pathways, presenting a wide range of possible intermediates and end products, with selectivity toward either CO or methane being of particular interest. We investigate in detail the reaction mechanism of CO2 hydrogenation to the single-carbon (C1) products on the Rh(111) facet by performing periodic density functional theory (DFT) calculations and kinetic Monte Carlo (kMC) simulations, which account for the adsorbate interactions through a cluster expansion approach. We observe that Rh readily facilitates the dissociation of hydrogen, thus contributing to the subsequent hydrogenation processes. The reverse water-gas shift (RWGS) reaction occurs via three different reaction pathways, with CO hydrogenation to the COH intermediate being a key step for CO2 methanation. The effects of temperature, pressure, and the composition ratio of the gas reactant feed are considered. Temperature plays a pivotal role in determining the surface coverage and adsorbate composition, with competitive adsorption between CO and H species influencing the product distribution. The observed adlayer configurations indicate that the adsorbed CO species are separated by adsorbed H atoms, with a high ratio of H to CO coverage on the Rh(111) surface being essential to promote CO2 methanation.

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models

Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems.

Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at ab initio accuracy using an approach that combines the bond valence model, Gaussian process regression, and ab initio thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration

Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.

Predicting aqueous sorption of organic pollutants on microplastics with machine learning

Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs.

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P-H Reactions

The paper discussed the use of machine learning (ML) and quantum chemistry calculations to predict the transition state and yield of copper-catalyzed P-H insertion reactions. By analyzing a dataset of 120 experimental data points, the transition state was determined using density functional theory (DFT). ML algorithms were then applied to analyze 16 descriptors derived from the quantum chemical transition state to predict the product yield. Among the algorithms studied, the Support Vector Machine (SVM) achieved the highest prediction accuracy of 97%, with over 80% correlation in Leave-One-Out Cross-Validation (LOOCV). Sensitivity analysis was performed on each descriptor, and a comprehensive investigation of the reaction mechanism was conducted to better understand the transition state characteristics. Finally, the ML model was used to predict reaction plans for experimental design, demonstrating strong predictive performance in subsequent experimental validation.

CKineticsDB─An Extensible and FAIR Data Management Framework and Datahub for Multiscale Modeling in Heterogeneous Catalysis

A great advantage of computational research is its reproducibility and reusability. However, an enormous amount of computational research data in heterogeneous catalysis is barricaded due to logistical limitations. Sufficient provenance and characterization of data and computational environment, with uniform organization and easy accessibility, can allow the development of software tools for integration across the multiscale modeling workflow. Here, we develop the Chemical Kinetics Database, CKineticsDB, a state-of-the-art datahub for multiscale modeling, designed to be compliant with the FAIR guiding principles for scientific data management. CKineticsDB utilizes a MongoDB back-end for extensibility and adaptation to varying data formats, with a referencing-based data model to reduce redundancy in storage. We have developed a Python software program for data processing operations and with built-in features to extract data for common applications. CKineticsDB evaluates the incoming data for quality and uniformity, retains curated information from simulations, enables accurate regeneration of publication results, optimizes storage, and allows the selective retrieval of files based on domain-relevant catalyst and simulation parameters. CKineticsDB provides data from multiple scales of theory (ab initio calculations, thermochemistry, and microkinetic models) to accelerate the development of new reaction pathways, kinetic analysis of reaction mechanisms, and catalysis discovery, along with several data-driven applications.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Designing main-group catalysts for low-temperature methane combustion by ozone

The catalytic combustion of methane at a low temperature is becoming increasingly key to controlling unburned CH₄ emissions from natural gas vehicles and power plants, although the low activity of benchmark platinum-group-metal catalysts hinders its broad application. Based on automated reaction route mapping, we explore main-group elements catalysts containing Si and Al for low-temperature CH₄ combustion with ozone. Computational screening of the active site predicts that strong Brønsted acid sites are promising for methane combustion. We experimentally demonstrate that catalysts containing strong Bronsted acid sites exhibit improved CH₄ conversion at 250 °C, correlating with the theoretical predictions. The main-group catalyst (proton-type beta zeolite) delivered a reaction rate that is 442 times higher than that of a benchmark catalyst (5 wt% Pd-loaded Al₂O₃) at 190 °C and exhibits higher tolerance to steam and SO₂. Our strategy demonstrates the rational design of earth-abundant catalysts based on automated reaction route mapping.

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, and nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth in data, the adoption and exploitation of artificial intelligence (AI) as part of the materials research framework have had a tremendous impact on the development of nanomaterials. AI has enabled revolutionary next-generation paradigms to significantly accelerate all stages of material discovery and facilitate the exploration of the enormous design space. In this review, we summarize recent advancements of AI applications in nanomaterials discovery, with a special emphasis on the selected applications of AI and nanotechnology for the net-zero emission future including the development of solar cells, hydrogen energy, battery materials for renewable energy, and CO₂ capture and conversion materials for carbon capture, utilization and storage (CCUS) technologies. In addition, we discuss the limitations and challenges of current AI applications in this area by identifying the gaps that exist in current development. Finally, we present the prospect for future research directions in order to facilitate the large-scale applications of artificial intelligence for advancements in nanomaterials.

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.

Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes

Unlike conventional liquid electrolytes, solid-state electrolytes (SSEs) have gained increased attention in the domain of all-solid-state lithium-ion batteries (ASSBs) due to their safety features, higher energy/power density, better electrochemical stability, and a broader electrochemical window. SSEs, however, face several difficulties, such as poorer ionic conductivity, complicated interfaces, and unstable physical characteristics. Vast research is still needed to find compatible and appropriate SSEs with improved properties for ASSBs. Traditional trial-and-error procedures to find novel and sophisticated SSEs require vast resources and time. Machine learning (ML), which has emerged as an effective and trustworthy tool for screening new functional materials, was recently used to forecast new SSEs for ASSBs. In this study, we developed an ML-based architecture to predict ionic conductivity by utilizing the characteristics of activation energy, operating temperature, lattice parameters, and unit cell volume of various SSEs. Additionally, the feature set can identify distinct patterns in the data set that can be verified using a correlation map. Because they are more reliable, the ensemble-based predictor models can more precisely forecast ionic conductivity. The prediction can be strengthened even further, and the overfitting issue can be resolved by stacking numerous ensemble models. The data set was split into 70:30 ratios to train and test with eight predictor models. The maximum mean-squared error and mean absolute error in training and testing for the random forest regressor (RFR) model were obtained as 0.001 and 0.003, respectively.

An automated reaction route mapping for the reaction of NO and active species on Ag4 clusters in zeolites

A computational investigation of the catalytic reaction on multinuclear sites is very challenging. Here, using an automated reaction route mapping method, the single-component artificial force induced reaction (SC-AFIR) algorithm, the catalytic reaction of NO and OH/OOH species over the Ag₄²⁺ cluster in a zeolite is investigated. The results of the reaction route mapping for H₂ + O₂ reveal that OH and OOH species are formed over the Ag₄²⁺ cluster via an activation barrier lower than that of OH formation from H₂O dissociation. Then, reaction route mapping is performed to examine the reactivity of the OH and OOH species with NO molecules over the Ag₄²⁺ cluster, resulting in the facile reaction path of HONO formation. With the aid of the automated reaction route mapping, the promotion effect of H₂ addition on the SCR reaction was computationally proposed (boosting the formation of OH and OOH species). In addition, the present study emphasizes that automated reaction route mapping is a powerful tool to elucidate the complicated reaction pathway on multi-nuclear clusters.

Next-generation intelligent laboratories for materials design and manufacturing

The contradiction between the importance of materials to modern society and their slow development process has led to the development of multiple methods to accelerate materials discovery. The recently emerged concept of intelligent laboratories integrates the developments in fields of high-throughput experimentation, automation, theoretical computing, and artificial intelligence to form a system that can autonomously carry out designed experiments and make scientific discoveries. We present the basic concepts and the foundations of this new research paradigm, demonstrate its typical application scenarios through case studies, and envision a collaborative human-machine meta laboratory in the future.

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) is a general-purpose method that was shown to achieve high accuracy for many applications with a speed close to its baseline semiempirical quantum mechanical (SQM) method ODM2*. Here, we evaluate the hitherto unknown performance of out-of-the-box AIQM1 without any refitting for reaction barrier heights on eight datasets, including a total of ∼24 thousand reactions. This evaluation shows that AIQM1's accuracy strongly depends on the type of transition state and ranges from excellent for rotation barriers to poor for, e.g., pericyclic reactions. AIQM1 clearly outperforms its baseline ODM2* method and, even more so, a popular universal potential, ANI-1ccx. Overall, however, AIQM1 accuracy largely remains similar to SQM methods (and B3LYP/6-31G* for most reaction types) suggesting that it is desirable to focus on improving AIQM1 performance for barrier heights in the future. We also show that the built-in uncertainty quantification helps in identifying confident predictions. The accuracy of confident AIQM1 predictions is approaching the level of popular density functional theory methods for most reaction types. Encouragingly, AIQM1 is rather robust for transition state optimizations, even for the type of reactions it struggles with the most. Single-point calculations with high-level methods on AIQM1-optimized geometries can be used to significantly improve barrier heights, which cannot be said for its baseline ODM2* method.

DFT-based Machine Learning for Ensemble Effect of Pd@Au Electrocatalysts on CO2 Reduction Reaction

The ensemble effect due to variation of Pd content in Pd-Au alloys have been widely investigated for several important reactions, including CO₂ reduction reaction (CO₂ RR), however, identifying the stable Pd arrangements on the alloyed surface and picking out the active sites are still challenging. Here we use a density functional theory (DFT) based machine-learning (ML) approach to efficiently find the low-energy configurations of Pd-Au(111) surface alloys and the potentially active sites for CO₂ RR, fully covering the Pd content from 0 to 100 %. The ML model is actively learning process to improve the predicting accuracy for the configuration formation energy and to find the stable Pd-Au(111) alloyed surfaces, respectively. The local surface properties of adsorption sites are classified into two classes by the K-means clustering approach, which are closely related to the Pd content on Au surface. The classification is reflected in the variation of adsorption energy of CO and H: In the low Pd content range (0-60 %) the adsorption energies over the surface alloys can be tuned significantly, and in the medium Pd content (37-68 %), the catalytic activity of surface alloys for CO₂ RR can be increased by increase the Pd content and attributed to the meta-stable active site over the surface. Thus, the active site-dependent reaction mechanism is elucidated based on the ensemble effect, which provides new physical insights to understand the surface-related properties of catalysts.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Unveiling the reaction pathways of hydrocarbons via experiments, computations and data science

Reaction networks of hydrocarbons are explored using first principles calculations, data science, and experiments. Transforming hydrocarbon data into networks reveals the prevalence of the formation and reaction of various molecules. Graph theory is implemented to extract knowledge from the reaction network. In particular, centralities analysis reveals that H⁺, CCC, CH₃⁺, CC, and [CH₂⁺]C have high degrees and are thus very likely to form or react with other molecules. Additionally, H⁺, CH₃⁺, C₂H₅⁺, C₈H₁₅⁺, C₈H₁₇⁺, and C₆H₁₁⁺ are found to have high control throughout the network and lead towards a series of additional reactions. The constructed network is also validated in experiments while the shortest path analysis is implemented for further comparison between experiment and the network. Thus, combining network analysis with first principles calculations uncovers key points in the development of various hydrocarbons that can be used to improve catalyst design and targeted synthesis of desired hydrocarbons.

Adsorption Energy in Oxygen Electrocatalysis

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for electrochemical reactions (e.g., water electrolysis, hydrogen fuel cell, electrochemical nitrogen fixation, electrochemical carbon dioxide reduction, etc.), which can bridge the gap between catalyst's structure and activity. Tracing the history and evolution of AE can help to understand electrocatalysis and design optimal electrocatalysts. Focusing on oxygen electrocatalysis, this review aims to provide a comprehensive introduction on how AE is selected as the activity descriptor, the intrinsic and empirical relationships related to AE, how AE links the structure and electrocatalytic performance, the approaches to obtain AE, the strategies to improve catalytic activity by modulating AE, the extrinsic influences on AE from the environment, and the methods in circumventing linear scaling relations of AE. An outlook is provided at the end with emphasis on possible future investigation related to the obstacles existing between adsorption energy and electrocatalytic performance.

A Rising 2D Star: Novel MBenes with Excellent Performance in Energy Conversion and Storage

As a flourishing member of the two-dimensional (2D) nanomaterial family, MXenes have shown great potential in various research areas. In recent years, the continued growth of interest in MXene derivatives, 2D transition metal borides (MBenes), has contributed to the emergence of this 2D material as a latecomer. Due to the excellent electrical conductivity, mechanical properties and electrical properties, thus MBenes attract more researchers' interest. Extensive experimental and theoretical studies have shown that they have exciting energy conversion and electrochemical storage potential. However, a comprehensive and systematic review of MBenes applications has not been available so far. For this reason, we present a comprehensive summary of recent advances in MBenes research. We started by summarizing the latest fabrication routes and excellent properties of MBenes. The focus will then turn to their exciting potential for energy storage and conversion. Finally, a brief summary of the challenges and opportunities for MBenes in future practical applications is presented.

Machine learning for a sustainable energy future

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

A Review on the Different Aspects and Challenges of the Dry Reforming of Methane (DRM) Reaction

The dry reforming of methane (DRM) reaction is among the most popular catalytic reactions for the production of syngas (H₂/CO) with a H₂:CO ratio favorable for the Fischer-Tropsch reaction; this makes the DRM reaction important from an industrial perspective, as unlimited possibilities for production of valuable products are presented by the FT process. At the same time, simultaneously tackling two major contributors to the greenhouse effect (CH₄ and CO₂) is an additional contribution of the DRM reaction. The main players in the DRM arena-Ni-supported catalysts-suffer from both coking and sintering, while the activation of the two reactants (CO₂ and CH₄) through different approaches merits further exploration, opening new pathways for innovation. In this review, different families of materials are explored and discussed, ranging from metal-supported catalysts, to layered materials, to organic frameworks. DRM catalyst design criteria-such as support basicity and surface area, bimetallic active sites and promoters, and metal-support interaction-are all discussed. To evaluate the reactivity of the surface and understand the energetics of the process, density-functional theory calculations are used as a unique tool.

Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to Devices

Electrocatalytic production of hydrogen peroxide (H2O2) via the 2e- transfer route of the oxygen reduction reaction (ORR) offers a promising alternative to the energy-intensive anthraquinone process, which dominates current industrial-scale production of H2O2. The availability of cost-effective electrocatalysts exhibiting high activity, selectivity, and stability is imperative for the practical deployment of this process. Single-atom catalysts (SACs) featuring the characteristics of both homogeneous and heterogeneous catalysts are particularly well suited for H2O2 synthesis and thus, have been intensively investigated in the last few years. Herein, we present an in-depth review of the current trends for designing SACs for H2O2 production via the 2e- ORR route. We start from the electronic and geometric structures of SACs. Then, strategies for regulating these isolated metal sites and their coordination environments are presented in detail, since these fundamentally determine electrocatalytic performance. Subsequently, correlations between electronic structures and electrocatalytic performance of the materials are discussed. Furthermore, the factors that potentially impact the performance of SACs in H2O2 production are summarized. Finally, the challenges and opportunities for rational design of more targeted H2O2-producing SACs are highlighted. We hope this review will present the latest developments in this area and shed light on the design of advanced materials for electrochemical energy conversion.

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Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH₄ to CH₃ reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO₂, Mg/BaO, and Ti/BaO are found to result in high C₂ yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.