Machine Learning-Guided Discovery of Underlying Decisive Factors and New Mechanisms for the Design of Nonprecious Metal Electrocatalysts

Rational design of Fe, N co-doped porous carbon derived from conjugated microporous polymer as an electrocatalytic platform for oxygen reduction reaction

Porous iron-nitrogen-doped carbons (FeNC) offer a great platform for construction of cathodic oxygen reduction reaction (ORR) catalysts in fuel cells. However, challenges still remain regarding with the collapse of carbon-skeleton during pyrolysis, uneven distribution of active sites and aggregation of metal atoms. In this work, we synthesized Fe, N co-doped conjugated microporous polymer (FeN-CMP) through a facile bottom-up strategy using 1,3,5-triethynylbenzene and iron-chelated 3,8-dibromo-1,10-phenanthroline as monomers, ensuring the uniform coordination of N with Fe element in network. Then, the resulting FeN-CMP was treated by pyrolysis without structural collapse to obtain porous FeNC electrocatalyst for ORR. The most active catalyst was fabricated under 900 °C, which exhibits remarkable ORR activity in alkaline medium with half-wave potential of 0.796 V (18 mV and 105 mV positive deviation from the commercial Pt/C catalyst and post-doping catalyst), high selectivity with nearly 4e- transfer process and excellent methanol tolerance. Our study first developed porous FeNC electrocatalysts derived from Fe, N-anchoring CMPs based on pre-functionalization of monomers, which exhibits great potential as an alternative to commercial Pt/C catalyst for ORR, and provides a feasible strategy of developing multi-atoms doping catalysts for energy storage and conversion as well as heterogeneous catalysis.

Selective oxygen reduction reaction: mechanism understanding, catalyst design and practical application

The oxygen reduction reaction (ORR) is a key component for many clean energy technologies and other industrial processes. However, the low selectivity and the sluggish reaction kinetics of ORR catalysts have hampered the energy conversion efficiency and real application of these new technologies mentioned before. Recently, tremendous efforts have been made in mechanism understanding, electrocatalyst development and system design. Here, a comprehensive and critical review is provided to present the recent advances in the field of the electrocatalytic ORR. The two-electron and four-electron transfer catalytic mechanisms and key evaluation parameters of the ORR are discussed first. Then, the up-to-date synthetic strategies and in situ characterization techniques for ORR electrocatalysts are systematically summarized. Lastly, a brief overview of various renewable energy conversion devices and systems involving the ORR, including fuel cells, metal-air batteries, production of hydrogen peroxide and other chemical synthesis processes, along with some challenges and opportunities, is presented.

Machine learning in nanozymes: from design to application

Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.

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.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

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.

Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression

Developing activity descriptors via data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments. A universal activity descriptor to evaluate the oxygen evolution reaction (OER) performance of oxide perovskites free of calculations or experiments was constructed and reached high accuracy and generalization ability. Utilizing this descriptor with Bayesian-optimized parameters, a series of compelling double perovskites with excellent OER activity were predicted and further evaluated using first-principles calculations. Finally, the two ML-predicted nickel-based perovskites with the best OER activity were successfully synthesized and characterized experimentally. This work opens a new way to extend machine-learning material design by utilizing multiple data sources.

Analysis of the oxygen evolution activity of layered double hydroxides (LDHs) using machine learning guidance

Layered double hydroxides (LDHs) are excellent catalysts for the oxygen evolution reaction (OER) because of their tunable properties, including chemical composition and structural morphology. An interplay between these adjustable properties and other (including external) factors might not always benefit the OER catalytic activity of LDHs. Therefore, we applied machine learning algorithms to simulate the double-layer capacitance to understand how to design/tune LDHs with targeted catalytic properties. The key factors of solving this task were identified using the Shapley Additive explanation and cerium was identified as an effective element to modify the double-layer capacitance. We also compared different modelling methods to identify the most promising one and the results revealed that binary representation is better than directly applying atom numbers as inputs for chemical compositions. Overpotentials of LDH-based materials as predicted targets were also carefully examined and evaluated, and it turns out that overpotentials can be predicted when measurement conditions about overpotentials are added as features. Finally, to confirm our findings, we reviewed additional experimental literature data and used them to test our machine algorithms to predict LDH properties. This analysis confirmed the very credible and robust generalization ability of our final model capable of achieving accurate results even with a relatively small dataset.

How machine learning can accelerate electrocatalysis discovery and optimization

Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.

Advances in Low Pt Loading Membrane Electrode Assembly for Proton Exchange Membrane Fuel Cells

Hydrogen has the potential to be one of the solutions that can address environmental pollution and greenhouse emissions from traditional fossil fuels. However, high costs hinder its large-scale commercialization, particularly for enabling devices such as proton exchange membrane fuel cells (PEMFCs). The precious metal Pt is indispensable in boosting the oxygen reduction reaction (ORR) in cathode electrocatalysts from the most crucial component, i.e., the membrane electrode assembly (MEA). MEAs account for a considerable amount of the entire cost of PEMFCs. To address these bottlenecks, researchers either increase Pt utilization efficiency or produce MEAs with enhanced performance but less Pt. Only a few reviews that explain the approaches are available. This review summarizes advances in designing nanocatalysts and optimizing the catalyst layer structure to achieve low-Pt loading MEAs. Different strategies and their corresponding effectiveness, e.g., performance in half-cells or MEA, are summarized and compared. Finally, future directions are discussed and proposed, aiming at affordable, highly active, and durable PEMFCs.

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 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.

Issues and Opportunities Facing Aqueous Mn2+ /MnO2 -based Batteries

Aqueous Mn²⁺ /MnO₂ -based batteries have attracted enormous attentions in aqueous energy storage fields, owing to their high working voltage and theoretical capacity (616 mAh g^-1 ) brought by the two-electron reaction (Mn²⁺ /Mn⁴⁺ ). However, there are currently several tricky challenges facing Mn²⁺ /MnO₂ -based batteries: their complicated working mechanisms, existing issues, and optimization strategies. This Perspective aims to provide a mechanistic understanding and an overview of the insufficiency, optimization, and future development for Mn²⁺ /MnO₂ -based batteries. The existing issues and deficiency in Mn²⁺ /MnO₂ -based batteries have been systematically analyzed, and optimization strategies have also been rationally summarized and discussed with deep insights. Also, the often-overlooked optimized objects and aspects have been highlighted with unique perspectives. The proposals of testing methods and performance assessment are presented, containing different degradation mechanisms. Based on the above points, this Perspective will provide guidance and contribute to the further development of aqueous Mn²⁺ /MnO₂ -based batteries.

Effectively Increasing Pt Utilization Efficiency of the Membrane Electrode Assembly in Proton Exchange Membrane Fuel Cells through Multiparameter Optimization Guided by Machine Learning

Although proton exchange membrane fuel cells have received attention, the high costs associated with Pt-based catalysts in membrane electrode assemblies (MEAs) remain a huge obstacle for large-scale applications. To solve this urgent problem, the utilization efficiency of Pt in MEAs must be increased. Facing numerous interacting parameters in an attempt to keep experimental costs as low as possible, we innovatively introduce machine learning (ML) to achieve this goal. Nine different ML algorithms are trained on the experimental dataset from our laboratory to precisely predict the performance and Pt utilization (maximum R² = 0.973/0.968). To determine the best synthesis conditions, black-box interpretation methods are applied to provide reliable insights from both qualitative and quantitative perspectives. The optimized choices of ionomer/catalyst ratio, water content, organic solvent, catalyst loading, stirring method, solid content, and ultrasonic spraying flow rate are properly made with few experimental attempts under ML results' guidance. Promising Pt utilization of 0.147 gPt kW^-1 and a power density of 1.02 W cm^-2 are achieved at 0.6 V in a single cell (H₂/air) at an ultralow total loading of 0.15 mg Pt cm^-2. Therefore, this work contributes to the economy of hydrogen energy by paving the way for MEA optimization with complex parameters by ML.

Atomically Dispersed, Low-Coordinate Co-N Sites on Carbon Nanotubes as Inexpensive and Efficient Electrocatalysts for Hydrogen Evolution

Hydrogen produced using renewable electricity is considered the key to achieving a low-carbon energy economy. However, the large-scale application of electrochemical water splitting for hydrogen evolution currently requires expensive platinum-based catalysts. Therefore, it is important to develop efficient and stable catalysts based on the rich reserves of transition metals as alternatives. In this study, the authors prepare a carbon-nanotube material enriched with atomically dispersed CoN sites having uniquely low coordination numbers via the simple mixing, pyrolysis, and leaching of inexpensive precursors. These atomically dispersed low-coordinate CoN sites provide an overpotential of only 82 mV at 10 mA cm^-2 for the hydrogen evolution reaction (HER) under challenging acidic conditions and show excellent durability in accelerated stability tests. Theoretical simulations also confirm that these unique, low-coordinate CoN₂ sites have lower energy barriers in catalyzing the HER than Fe/NiN₂ sites and commonly reported CoN₃ /N₄ sites. Therefore, the method provides a new concept for the design of single-atom catalytic sites with low coordination numbers. It also serves to reduce the cost of hydrogen production in the future owing to the high catalytic activity, low cost, and scalable production process.