Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Metal Electrocatalysts for Hydrogen Production in Water Splitting

The rising demand for fossil fuels and the resulting pollution have raised environmental concerns about energy production. Undoubtedly, hydrogen is the best candidate for producing clean and sustainable energy now and in the future. Water splitting is a promising and efficient process for hydrogen production, where catalysts play a key role in the hydrogen evolution reaction (HER). HER electrocatalysis can be well performed by Pt with a low overpotential close to zero and a Tafel slope of about 30 mV dec-1. However, the main challenge in expanding the hydrogen production process is using efficient and inexpensive catalysts. Due to electrocatalytic activity and electrochemical stability, transition metal compounds are the best options for HER electrocatalysts. This study will focus on analyzing the current situation and recent advances in the design and development of nanostructured electrocatalysts for noble and non-noble metals in HER electrocatalysis. In general, strategies including doping, crystallization control, structural engineering, carbon nanomaterials, and increasing active sites by changing morphology are helpful to improve HER performance. Finally, the challenges and future perspectives in designing functional and stable electrocatalysts for HER in efficient hydrogen production from water-splitting electrolysis will be described.

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.

A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts

Transition metal borides, carbides, pnictides, and chalcogenides (X-ides) have emerged as a class of materials for the oxygen evolution reaction (OER). Because of their high earth abundance, electrical conductivity, and OER performance, these electrocatalysts have the potential to enable the practical application of green energy conversion and storage. Under OER potentials, X-ide electrocatalysts demonstrate various degrees of oxidation resistance due to their differences in chemical composition, crystal structure, and morphology. Depending on their resistance to oxidation, these catalysts will fall into one of three post-OER electrocatalyst categories: fully oxidized oxide/(oxy)hydroxide material, partially oxidized core@shell structure, and unoxidized material. In the past ten years (from 2013 to 2022), over 890 peer-reviewed research papers have focused on X-ide OER electrocatalysts. Previous review papers have provided limited conclusions and have omitted the significance of "catalytically active sites/species/phases" in X-ide OER electrocatalysts. In this review, a comprehensive summary of (i) experimental parameters (e.g., substrates, electrocatalyst loading amounts, geometric overpotentials, Tafel slopes, etc.) and (ii) electrochemical stability tests and post-analyses in X-ide OER electrocatalyst publications from 2013 to 2022 is provided. Both mono and polyanion X-ides are discussed and classified with respect to their material transformation during the OER. Special analytical techniques employed to study X-ide reconstruction are also evaluated. Additionally, future challenges and questions yet to be answered are provided in each section. This review aims to provide researchers with a toolkit to approach X-ide OER electrocatalyst research and to showcase necessary avenues for future investigation.

Machine Learning Descriptors for Data-Driven Catalysis Study

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Prediction of Three-Metal Cluster Catalysts on Two-Dimensional W2N3 Support with Integrated Descriptors for Electrocatalytic Nitrogen Reduction

In the electrocatalytic nitrogen reduction reaction (NRR), nitrogen (N2) is chemically inert, it is difficult to break the triple bond, and the subsequent protonation step is very challenging. Suitable catalysts with high selectivity and high activity are needed to promote the electrocatalytic NRR. We screen a large number of clusters composed of three metal atoms embedded into a two-dimensional metal nitride, W2N3, with a N vacancy, and calculate the reaction energetics. The VNiCu cluster has the best catalytic activity among all the catalysts proposed so far. The Fe3 and Fe2Co clusters are excellent catalysts as well. In all cases, spin polarization is needed to observe the catalytic effect. We establish the optimal NRR path and confirm that it remains unchanged in the presence of a solvent. We find three groups of descriptors that can well predict the materials' properties and exhibit linear relationships with the NRR limiting potential.

Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.

Machine Learning: A New Paradigm in Computational Electrocatalysis

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.

Prediction of bimetal embedded in two-dimensional materials for CO2 reduction electrocatalysis with a new integrated descriptor

CO₂ reduction catalysis plays an important role in the process of converting harmful exhaust gas into useful fuels. However, the product complexity and the difficult hydrogenation in critical steps make it difficult to find a suitable catalyst for CO₂ reduction. In this work, we report homo/hetero bimetal embedded in two-dimensional materials for electrocatalysis and discovered a new descriptor. We chose β₁₂-borophene accommodating two transition metal atoms for efficient CO₂RR as a model system. We found that MnCo and VV systems are promising for CO₂ reduction with good stability and high selectivity over HER. Through least absolute shrinkage and selection operator (LASSO) regression, we discovered a new integrated descriptor containing the spin moment of the metals and the descriptor is linked with the performance of the first step of CO₂ hydrogenation. The MnCo system could catalyze a C1 process with low free energy change of the rate determining step. The VV system could also conduct the C2 process with low free energy change of the rate determining step. Bader charge analysis shows the ability of the borophene substrate to provide or hold electrons. This work demonstrates homonuclear and heteronuclear biatomic catalysts with high activity for CO₂RR.

Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia

The conversion of nitrogen to ammonia offers a sustainable and environmentally friendly approach for producing precursors for fertilizers and efficient energy carriers. Owing to the large energy density and significant gravimetric hydrogen content, NH₃ is considered an apt next-generation energy carrier and liquid fuel. However, the low conversion efficiency and slow production of ammonia through the nitrogen reduction reaction (NRR) are currently bottlenecks, making it an unviable alternative to the traditional Haber-Bosch process for ammonia production. The rational design and engineering of catalysts (both photo- and electro-) represent a crucial challenge for improving the efficiency and exploiting the full capability of the NRR. In the present review, we highlight recent progress in the development of graphene-based systems and graphene derivatives as catalysts for the NRR. Initially, the history, fundamental mechanism, and importance of the NRR to produce ammonia are briefly discussed. We also outline how surface functionalization, defects, and hybrid structures (single-atom/multiatom as well as composites) affect the N₂ conversion efficiency. The potential of graphene and graphene derivatives as NRR catalysts is highlighted using pertinent examples from theoretical simulations as well as machine learning based performance predictive methods. The review is concluded by identifying the crucial advantages, drawbacks, and challenges associated with principal scientific and technological breakthroughs in ambient catalytic NRR.

Main Descriptors To Correlate Structures with the Performances of Electrocatalysts

Traditional trial and error approaches to search for hydrogen/oxygen redox catalysts with high activity and stability are typically tedious and inefficient. There is an urgent need to identify the most important parameters that determine the catalytic performance and so enable the development of design strategies for catalysts. In the past decades, several descriptors have been developed to unravel structure-performance relationships. This Minireview summarizes reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discusses their merits/limitations. Understanding the trends in electrocatalytic performance and predicting promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning have also been adopted to discover new and advanced descriptors. Finally, linear scaling relationships are analyzed and several strategies proposed to circumvent the established scaling relationships and overcome the constraints imposed on the catalytic performance.

Latent Representation Learning for Structural Characterization of Catalysts

Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum-structure relationship that can be eventually mapped to physicochemical properties. The latent space of the autoencoder also provides a pathway to interpret the information content "hidden" in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes.

Prediction of Dual-Doped Integrated CsPbBr3-CsPbCl3 Perovskite Heterostructure for Practical Photocatalytic Water Splitting with a New Descriptor

Photocatalytic water splitting has always been a field where breakthroughs are expected to solve energy and environmental problems. However, current catalysts suffer from low activity in mismatched catalytic environments and high cost. Herein, we designed a series of integrated CsPbBr₃-CsPbCl₃ heterostructures to explore their catalytic capability. Based on extensive calculations, we discovered the inner connection between dopant atoms and the catalytic performance and proposed a new descriptor by applying the Least Absolute Shrinkage and Selection Operator (LASSO) analysis. After systematic screening, the CsPbBr₃:Ni-CsPbCl₃:Co system is found to be promising for single-catalyst overall water splitting under the same environment. Furthermore, a smaller bandgap that covers the redox potential of water splitting suggests the capability for photocatalysis. Besides, the CsPbBr₃:Ni-CsPbCl₃:Co system bulk-doped by Co could conduct the photocatalysis with better performance.

Transition Metal Dichalcogenides for the Application of Pollution Reduction: A Review

The material characteristics and properties of transition metal dichalcogenide (TMDCs) have gained research interest in various fields, such as electronics, catalytic, and energy storage. In particular, many researchers have been focusing on the applications of TMDCs in dealing with environmental pollution. TMDCs provide a unique opportunity to develop higher-value applications related to environmental matters. This work highlights the applications of TMDCs contributing to pollution reduction in (i) gas sensing technology, (ii) gas adsorption and removal, (iii) wastewater treatment, (iv) fuel cleaning, and (v) carbon dioxide valorization and conversion. Overall, the applications of TMDCs have successfully demonstrated the advantages of contributing to environmental conversation due to their special properties. The challenges and bottlenecks of implementing TMDCs in the actual industry are also highlighted. More efforts need to be devoted to overcoming the hurdles to maximize the potential of TMDCs implementation in the industry.

The Potential of Overlayers on Tin-based Perovskites for Water Splitting

Photoelectrochemical water splitting is a promising method of clean hydrogen production for green energy uses. Here, we report on a tin-based oxide perovskite combined with an overlayer that shows enhanced bifunctional hydrogen and oxygen evolution. In our first-principles study of tin-based perovskites, based upon density functional theory, we investigate how the formation of a surface affects the electronic properties of these materials. We show that the best candidate, SrSnO₃, possesses hydrogen and oxygen overpotentials of 0.75 and 0.72 eV, respectively, which are reduced to 0.35 and 0.54 eV with the inclusion of a ZrO₂ overlayer. Furthermore, this overlayer promotes charge extraction, stabilizes the reaction pathways, and improves the band gap such that it straddles the overpotentials between pH 0 and pH 12. This result indicates that SrSnO₃ with a ZrO₂ overlayer has significant potential as a highly efficient bifunctional water splitter for producing hydrogen and oxygen gas on the same surface.

Graphene Activation Explains the Enhanced Hydrogen Evolution on Graphene-Coated Molybdenum Carbide Electrocatalysts

Molybdenum carbides (Mo_xC) have shown high catalytic activities toward hydrogen evolution reaction (HER) when coupled with graphene. Herein, we use density functional theory (DFT) calculations in conjunction with ab initio thermodynamics and electrochemical modeling on γ-MoC supported graphene to determine the origin of the enhanced HER activities. In addition to previous claims that graphene's main role is to prevent agglomeration of Mo_xC nanoparticles, we show that the interplay between γ-MoC coupling and graphene defect chemistry activates graphene for the HER. For γ-MoC supported graphene systems, the HER mechanism follows the Volmer-Heyrovsky pathway with the Heyrovsky reaction as the rate-determining step. To simulate the electrochemical linear sweep voltammetry at the device level, we develop a computational current model purely from the thermodynamic and kinetics descriptors obtained using DFT. This model shows that γ-MoC supported graphene with divacancies is optimum for HER with an exchange current density of ∼1 × 10^-4 A/cm² and Tafel slope of ∼50 mV/dec^-1, which are in good agreement with experimental results.