Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

Empowering Generalist Material Intelligence with Large Language Models

Large language models (LLMs) are steering the development of generalist materials intelligence (GMI), a unified framework integrating conceptual reasoning, computational modeling, and experimental validation. Central to this framework is the agent-in-the-loop paradigm, where LLM-based agents function as dynamic orchestrators, synthesizing multimodal knowledge, specialized models, and experimental robotics to enable fully autonomous discovery. Drawing from a comprehensive review of LLMs' transformative impact across representative applications in materials science, including data extraction, property prediction, structure generation, synthesis planning, and self-driven labs, this study underscores how LLMs are revolutionizing traditional tasks, catalyzing the agent-in-the-loop paradigm, and bridging the ontology-concept-computation-experiment continuum. Then the unique challenges of scaling up LLM adoption are discussed, particularly those arising from the misalignment of foundation LLMs with materials-specific knowledge, emphasizing the need to enhance adaptability, efficiency, sustainability, interpretability, and trustworthiness in the pursuit of GMI. Nonetheless, it is important to recognize that LLMs are not universally efficient. Their substantial resource demands and inconsistent performance call for careful deployment based on demonstrated task suitability. To address these realities, actionable strategies and a progressive roadmap for equitably and democratically implementing materials-aware LLMs in real-world practices are proposed.

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.

The role of soil chemical properties and microbial communities on Dendrocalamus brandisii bamboo shoot quality, Yunnan Province, China

Objective

To explore the effects of soil nutrients and microbial communities on the quality of Dendrocalamus brandisii shoots in different regions, providing a scientific basis for their development and utilization.

Methods

Using seven different geographic sources of D. brandisii from Yunnan Province as research subjects, this study employs chemical analysis and high-throughput sequencing to reveal the relationship between soil nutrients, microbial functional groups, and the nutritional quality of bamboo shoots.

Results

The results indicate that there are significant differences in soil nutrient content among the regions (p < 0.05), with bamboo shoots from Baoshan Changning (CN) exhibiting the best overall nutritional quality. The key factors influencing bacterial community changes include pH, available phosphorus (AP), and available potassium (AK). In contrast, the main factors affecting fungal community changes are pH, soil organic matter (SOM), available potassium (AK), and total nitrogen (TN). This version maintains clarity and logical flow, making it easier for readers to understand the different factors influencing bacterial and fungal community changes. The diversity indices of soil microbial communities among different sources of Dendrocalamus brandisii show significant differences (p < 0.05). The dominant groups in the seven regions include Proteobacteria, Acidobacteriota, Actinobacteriota, Chloroflexi, Ascomycota, and Basidiomycota. The soil microbial community in Baoshan Changning (CN) shows significant structural differences compared to the other six regions, with the highest relative abundances of Chloroflexi and Acidobacteriota. In contrast, the highest relative abundance of Proteobacteria is found in Honghe Shiping (SP), while Actinobacteriota has the highest relative abundance in Yuxi Xinping (XP). RDA analysis indicates that soil nutrients (SOM, pH, AP, TN) affect the water content, soluble sugar, and crude fat of bamboo shoots. Additionally, the bacterial communities including Actinobacteriota, Chloroflexi, Patescibacteria, GAL15, and Cyanobacteria influence the water content, soluble sugar, ash content, protein, and lignin of bamboo shoots.

Discussion

In the fungal community, Basidiomycota, Kickxellomycota, Mucoromycota, unclassified-k-Fungi, and Glomeromycota affect the water content and tannin levels in bamboo shoots. In summary, soil nutrients and soil microorganisms are interconnected and work together to influence the quality of bamboo shoots.

From Mining to Manufacturing: Scientific Challenges and Opportunities behind Battery Production

This Review explores the status and progress made over the past decade in the areas of raw material mining, battery materials and components scale-up, processing, and manufacturing. While substantial advancements have been achieved in understanding battery materials, the transition to large-scale manufacturing introduces scientific challenges that must be addressed from multiple perspectives. Rather than focusing on new material discoveries or incremental performance improvements, this Review focuses on the critical issues that arise in battery manufacturing and highlights the importance of cost-oriented fundamental research to bridge the knowledge gap between fundamental research and industrial production. Challenges and opportunities in integrating machine learning (ML) and artificial intelligence (AI) to digitalize the manufacturing process and eventually realize fully autonomous production are discussed. The review also emphasizes the pressing need for workforce development to meet the growing demands of the battery industry. Potential strategies are suggested for accelerating the manufacturing of current and future battery technologies, ensuring that the workforce is equipped with the necessary skills to support research, development, and large-scale production.

Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes

LiNi0.5Mn1.5O4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6-4.7 V vs Li+/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.

A high-throughput experimentation platform for data-driven discovery in electrochemistry

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput electrochemical characterization (AHTech) platform as a cost-effective and versatile tool for rapidly assessing liquid analytes. The Python-controlled platform combines a liquid handling robot, potentiostat, and customizable microelectrode bundles for diverse, reproducible electrochemical measurements in microtiter plates, minimizing chemical consumption and manual effort. To showcase the capability of AHTech, we screened a library of 180 small molecules as electrolyte additives for aqueous zinc metal batteries, generating data for training machine learning models to predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations and Spearman's correlation, pinpointing high-performance candidates like cis-4-hydroxy-d-proline, which achieved an average Coulombic efficiency of 99.52% over 200 cycles. The workflow established herein is highly adaptable, offering a powerful framework for accelerating the exploration and optimization of extensive chemical spaces across diverse energy storage and conversion fields.

Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry

Molecular electrocatalysis campaigns often require tuning multiple experimental parameters to obtain kinetically insightful electrochemical measurements, a prohibitively time-consuming task when performing comprehensive studies across multiple catalysts and substrates. In this work, we present an autonomous workflow that combines Bayesian optimization and automated electrochemistry to perform fully unsupervised cyclic voltammetry (CV) studies of molecular electrocatalysis. We developed CV descriptors that leveraged the conceptual framework of the EC' (where EC' denotes an electrochemical step followed by a catalytic chemical step) kinetic zone diagram to enable efficient Bayesian optimization. The CV descriptor's effect on optimization performance was evaluated using a digital twin of our autonomous experimental platform, quantifying the accuracy of obtained kinetic values against the known ground truth. We demonstrated our platform experimentally by performing autonomous studies of TEMPO-catalyzed ethanol and isopropanol electro-oxidation, demonstrating rapid identification of kinetically insightful conditions in 10 or less iterations through the closed-loop workflow. Overall, this work highlights the application of autonomous electrochemical platforms to accelerate mechanistic studies in molecular electrocatalysis and beyond.

Excess density as a descriptor for electrolyte solvent design

Electrolytes mediate interactions between the cathode and anode and determine the performance characteristics of batteries. The mixtures of multiple solvents are often used in electrolytes to achieve the desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here, we investigate the excess density of commonly used Li-ion battery solvents, such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. The mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to the mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our automated test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using smooth overlap of atomic position fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

PANDA: a self-driving lab for studying electrodeposited polymer films

We introduce the polymer analysis and discovery array (PANDA), an automated system for high-throughput electrodeposition and functional characterization of polymer films. The PANDA is a custom, modular, and low-cost system based on a CNC gantry that we have modified to include a syringe pump, potentiostat, and camera with a telecentric lens. This system can perform fluid handling, electrochemistry, and transmission optical measurements on samples in custom 96-well plates that feature transparent and conducting bottoms. We begin by validating this platform through a series of control fluid handling and electrochemistry experiments to quantify the repeatability, lack of cross-contamination, and accuracy of the system. As a proof-of-concept experimental campaign to study the functional properties of a model polymer film, we optimize the electrochromic switching of electrodeposited poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films. In particular, we explore the monomer concentration, deposition time, and deposition voltage using an array of experiments selected by Latin hypercube sampling. Subsequently, we run an active learning campaign based upon Bayesian optimization to find the processing conditions that lead to the highest electrochromic switching of PEDOT:PSS. This self-driving lab integrates optical and electrochemical characterization to constitute a novel, automated approach for studying functional polymer films.

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.

CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations

Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven accelerated electrolyte discovery efforts, we curated and analyzed a large dataset covering a wide range of experimentally recorded ionic conductivities for various combinations of lithium salts, solvents, concentrations, and temperatures. The dataset is named as 'Conductivity Atlas for Lithium salts and Solvents' (CALiSol-23). Comprehensive datasets are lacking but are critical to building chemistry agnostic machine learning models for conductivity as well as data driven electrolyte optimization tasks. CALiSol-23 was derived from an exhaustive review of literature concerning experimental non-aqueous electrolyte conductivity measurement. The final dataset consists of 13,825 individual data points from 27 different experimental articles, in total covering 38 solvents, a broad temperature range, and 14 lithium salts. CALiSol-23 can help expedite machine learning model development that can help in understanding the complexities of ion transport and streamlining the optimization of non-aqueous electrolyte mixtures.

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Advances of LiCoO2 in Cathode of Aqueous Lithium-Ion Batteries

Aqueous lithium-ion batteries offer promising advantages such as low cost, enhanced safety, high rate capability, and the ability to deliver considerable capacity at 1.8 V, making them ideal candidates for large-scale reserve power sources for renewable energy. However, the practical application of aqueous lithium-ion batteries has been hindered by the poor cycle stability of layered cathode materials, including LiCoO2, in neutral aqueous electrolytes. This review examines the working principles, material limitations, and research progress of aqueous lithium-ion batteries. The types and characteristics of materials used in the cathode of aqueous lithium-ion batteries are summarized, with a primary focus on the attenuation mechanisms of LiCoO2 when used as the cathode material in aqueous electrolytes. Furthermore, this review explores the advancements in utilizing LiCoO2 in the cathode of aqueous lithium-ion batteries, as well as the combination with machine learning. By addressing these critical aspects, this review aims to provide a comprehensive understanding of aqueous lithium-ion batteries and shed light on future development and application prospects.

Building Smarter Aqueous Batteries

Amidst the global trend of advancing renewable energies toward carbon neutrality, energy storage becomes increasingly critical due to the intermittency of renewables. As an alternative to lithium-ion batteries (LIBs), aqueous batteries have received growing attention for large-scale energy storage due to their economical and safe features. Despite the fruitful achievements at the material level, the reliability and lifetime of aqueous batteries are still far from satisfactory. Alike LIBs, integrating smartness is essential for more reliable and long-life aqueous batteries via operando monitoring and automatic response to extreme abuses. In this review, recent advances in sensing techniques and multifunctional battery-sensor systems together with self-healing methods in aqueous batteries is summarized. The significant role of artificial intelligence in designing and optimizing aqueous batteries with high efficiency is also highlighted. Ultimately, it is extrapolated toward the future and present the humble perspective for building smarter aqueous batteries.

Machine intelligence-accelerated discovery of all-natural plastic substitutes

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Ether-Based High-Voltage Lithium Metal Batteries: The Road to Commercialization

Ether-based high-voltage lithium metal batteries (HV-LMBs) are drawing growing interest due to their high compatibility with the Li metal anode. However, the commercialization of ether-based HV-LMBs still faces many challenges, including short cycle life, limited safety, and complex failure mechanisms. In this Review, we discuss recent progress achieved in ether-based electrolytes for HV-LMBs and propose a systematic design principle for the electrolyte based on three important parameters: electrochemical performance, safety, and industrial scalability. Finally, we summarize the challenges for the commercial application of ether-based HV-LMBs and suggest a roadmap for future development.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

How machine learning can extend electroanalytical measurements beyond analytical interpretation

Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb-Wagner test on a magnesium spinel intercalation host. As compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.

Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity

Solution-processed inorganic solar cells with less toxic and earth-abundant elements are emerging as viable alternatives to high-performance lead-halide perovskite solar cells. However, the wide range of elements and process parameters impede the rapid exploration of vast chemical spaces. Here, we developed an automated robot-embedded measurement system that performs photoabsorption spectroscopy, optical microscopy, and white-light flash time-resolved microwave conductivity (TRMC). We tested 576 films of quaternary element-blended wide-bandgap Cs-Bi-Sb-I semiconductors with various compositions, organic salt additives (MACl, FACl, MAI, and FAI, where MA and FA represent methylammonium and formamidinium, respectively), and thermal annealing temperatures. Among them, we found that the maximum power conversion efficiency (PCE) was 2.36%, which is significantly higher than the PCE of 0.68% for a reference film without an additive. Machine learning (ML) and statistical analyses revealed significant features and their relationships with TRMC transients, thereby demonstrating the advantages of combining ML and automated experiments for the high-throughput exploration of photovoltaic materials.

Mechanistic understanding of the correlation between structure and dynamics of liquid carbonate electrolytes: impact of polarization

Liquid electrolyte design and modelling is an essential part of the development of improved lithium ion batteries. For mixed organic carbonates (ethylene carbonate (EC) and ethyl-methyl carbonate (EMC) mixtures)-based electrolytes with LiPF₆ as salt, we have compared a polarizable force field with the standard non-polarizable force field with and without charge rescaling to model the structural and dynamic properties. The result of our molecular dynamics simulations shows that both polarizable and non-polarizable force fields have similar structural factors, which are also in agreement with X-ray diffraction experimental results. In contrast, structural differences are observed for the lithium neighborhood, while the lithium-anion neighbourhood is much more pronounced for the polarizable force field. Comparison of EC/EMC coordination statistics with Fourier transformed infrared spectroscopy (FTIR) shows the best agreement for the polarizable force field. Also for transport quantities such as ionic conductivities, transference numbers, and viscosities, the agreement with the polarizable force field is by far better for a large range of salt concentrations and EC : EMC ratios. In contrast, for the non-polarizable variants, the dynamics are largely underestimated. The excellent performance of the polarizable force field is explored in different ways to pave the way to a realistic description of the structure-dynamics relationships for a wide range of salt and solvent compositions for this standard electrolyte. In particular, we can characterize the distinct correlation terms between like and unlike ions, relate them to structural properties, and explore to which degree the transport in this electrolyte is mass or charge limited.

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.

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.