Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters

High-throughput screening of bifunctional catalysts for oxygen evolution/reduction reaction at the subnanometer regime

The development of low-cost, stable, and highly efficient electrocatalysts for the bifunctional oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is crucial for advancing future renewable technologies. In this study, we systematically investigated the OER and ORR performance of subnano clusters across the 3d, 4d, and 5d transition metal (TM) series of varying sizes using first-principles calculations. The fluxional identity of these clusters in the subnanometer regime is reflected in their non-monotonic catalytic activity. We established a size-dependent scaling relationship between OER/ORR intermediates, leading to a reshaping of the activity volcano plot at the subnanometer scale. Our detailed mechanistic investigation revealed a shift in the apex of the activity volcano from the Pt(111) and IrO2 surfaces to the Au11 clusters for both OER and ORR. Late transition metal subnano clusters, specifically Au11, emerged as the best bifunctional electrocatalyst, demonstrating significantly lower overpotential values. Furthermore, we categorized our catalysts into three clusters and employed the Random Forest Regression method to evaluate the impact of non-ab initio electronic features on OER and ORR activities. Interestingly, d-band filling emerged as the primary contributor to the bifunctional activity of the subnano clusters. This work not only provides a comprehensive view of OER and ORR activities but also presents a new pathway for designing and discovering highly efficient bifunctional catalysts.

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction

In computational heterogeneous catalysis, Sabatier's principle-based activity volcano plots provide an intuitive guide to catalyst design but impose a fundamental constraint on the maximum achievable catalytic performance. Recently, subnano clusters have emerged as an exciting platform, offering high noble metal utilization and superior performance for various reactions compared to extended surfaces, reflecting a complex structure-activity relationship in the non-scalable regime. However, understanding their non-monotonic catalytic activity, attributed to the large configurational space and their fluxional identity, poses a formidable challenge. Here, we present a machine learning (ML) framework that captures the non-monotonic trends in oxygen reduction reaction (ORR) activity at the subnanometer scale, attributed to their dynamic fluxional characteristics. We demonstrate a size-dependent shifting and reshaping of the ORR activity volcano, with Au replacing Pt at the peak. Leveraging only upon the non-ab initio geometric and electronic properties, our trained ML model accurately captures the site-specific adsorption energies of intermediates at the subnanometer regime. To account for the inconsistent trend in activity, we analyzed the correlation between electronic and geometric properties. Our findings reveal that the d-filling and coupling matrix of the neighboring metal atom significantly influences the intermediate adsorption on the local chemical environment compared to the d-band center. Following this analysis, we utilized ML to map the catalyst distribution in the activity volcano and identified the five best sub-nano electrocatalysts, demonstrating overpotential values lower than or comparable to the Pt(111) surface for the ORR. This study provides intuitive guidelines for the rational designing of highly efficient electrocatalysts for fuel cell applications while modifying the activity volcano plots for electrocatalysts at the subnanometer regime.

Hydrogen, Oxygen, and Lead Adsorbates on Al13Co4(100): Accurate Potential Energy Surfaces at Low Computational Cost by Machine Learning and DFT-Based Data

Intermetallic compounds are promising materials in numerous fields, especially those involving surface interactions, such as catalysis. A key factor to investigate their surface properties lies in adsorption energy maps, typically built using first-principles approaches. However, exploring the adsorption energy landscapes of intermetallic compounds can be cumbersome, usually requiring huge computational resources. In this work, we propose an efficient method to predict adsorption energies, based on a Machine Learning (ML) scheme fed by a few Density Functional Theory (DFT) estimates performed on n sites selected through the Farthest Point Sampling (FPS) process. We detail its application on the Al13Co4(100) quasicrystalline approximant surface for several atomic adsorbates (H, O, and Pb). On this specific example, our approach is shown to outperform both simple interpolation strategies and the recent ML force field MACE [arXiv.2206.07697], especially when the number n is small, i.e., below 36 sites. The ground-truth DFT adsorption energies are much more correlated with the predicted FPS-ML estimates (Pearson R-factor of 0.71, 0.73, and 0.90 for H, O and Pb, respectively, when n = 36) than with interpolation-based or MACE-ML ones (Pearson R-factors of 0.43, 0.39, and 0.56 for H, O, and Pb, in the former case and 0.22, 0.35, and 0.63 in the latter case). The unbiased root-mean-square error (ubRMSE) is lower for FPS-ML than for interpolation-based and MACE-ML predictions (0.15, 0.17, and 0.17 eV, respectively, for hydrogen and 0.17, 0.25, and 0.22 eV for lead), except for oxygen (0.55, 0.47, and 0.46 eV) due to large surface relaxations in this case. We believe that these findings and the corresponding methodology can be extended to a wide range of systems, which will motivate the discovery of novel functional materials.

Transferable, Living Data Sets for Predicting Global Minimum Energy Nanocluster Geometries

Modeling of nanocluster geometries is essential for studying the dependence of catalytic activity on the available active sites. In heterogeneous catalysis, the interfacial interaction of the support with the metal can result in modification of the structural and electronic properties of the clusters. To tackle the study of a diverse array of cluster shapes, data-driven methodologies are essential to circumvent prohibitive computational costs. At their core, these methods require large data sets in order to achieve the necessary accuracy to drive structural exploration. Given the similarity in binding character of the transition metals, cluster shapes encountered for various systems show a large amount of overlap. This overlap has been utilized to construct a living data set which may be carried over across multiple studies. Iterative refinement of this data set provides a low-cost pathway for initialization of cluster studies. It is shown that utilization of transferable structural information can reduce model construction costs by more than 90%. The benefits of this approach are particularly notable for alloy systems, which possess significantly larger configurational spaces compared to the pure-phase counterparts.

Hydrogen evolution descriptors of 55-atom PtNi nanoclusters and interaction with graphite

Density functional simulations have been performed for PtnNi55-nclusters (n=0,12,20,28,42,55) to investigate their catalytic properties for the hydrogen evolution reaction (HER). Starting from the icosahedralPt12Ni43, hydrogen adsorption energetics and electronicd-band descriptors indicate HER activity comparable to that of purePt55(distorted reduced core structure). The PtNi clusters accommodate a large number of adsorbed hydrogen before reaching a saturated coverage, corresponding to 3-4 H atoms per icosahedron facet (in total ∼70-80). The differential adsorption free energies are well within the window of|ΔGH|<0.1 eV which is considered optimal for HER. The electronic descriptors show similarities with the platinumd-band, although the uncovered PtNi clusters are magnetic. Increasing hydrogen coverage suppresses magnetism and depletes electron density, resulting in expansion of the PtNi clusters. For a single H atom, the adsorption free energy varies between -0.32 (Pt12Ni43) and -0.59 eV (Pt55). The most stable adsorption site is Pt-Pt bridge for Pt-rich compositions and a hollow site surrounded by three Ni for Pt-poor compositions. A hydrogen molecule dissociates spontaneously on the Pt-rich clusters. The above HER activity predictions can be extended to PtNi on carbon support as the interaction with a graphite model structure (w/o vacancy defect) results in minor changes in the cluster properties only. The cluster-surface interaction is the strongest forPt55due to its large facing facet and associated van der Waals forces.

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.

Computational Approaches to Alkaline Anion-Exchange Membranes for Fuel Cell Applications

Anion-exchange membranes (AEMs) are key components in relatively novel technologies such as alkaline exchange-based membrane fuel cells and AEM-based water electrolyzers. The application of AEMs in these processes is made possible in an alkaline environment, where hydroxide ions (OH^-) play the role of charge carriers in the presence of an electrocatalyst and an AEM acts as an electrical insulator blocking the transport of electrons, thereby preventing circuit break. Thus, a good AEM would allow the selective transport of OH^- while preventing fuel (e.g., hydrogen, alcohol) crossover. These issues are the subjects of in-depth studies of AEMs-both experimental and theoretical studies-with particular emphasis on the ionic conductivity, ion exchange capacity, fuel crossover, durability, stability, and cell performance properties of AEMs. In this review article, the computational approaches used to investigate the properties of AEMs are discussed. The different modeling length scales are microscopic, mesoscopic, and macroscopic. The microscopic scale entails the ab initio and quantum mechanical modeling of alkaline AEMs. The mesoscopic scale entails using molecular dynamics simulations and other techniques to assess the alkaline electrolyte diffusion in AEMs, OH^- transport and chemical degradation in AEMs, ion exchange capacity of an AEM, as well as morphological microstructures. This review shows that computational approaches can be used to investigate different properties of AEMs and sheds light on how the different computational domains can be deployed to investigate AEM properties.

Determination of Pb2+ by Colorimetric Method Based on Catalytic Amplification of Ag Nanoparticles Supported by Covalent Organic Frameworks

In this paper, covalent organic frameworks (COFs) are prepared by solvothermal synthesis using 1,3,5-benzenetricarboxaldehyde and benzidine as ligands. Then, using COFs as a template, AgCOFs with high catalytic activity is prepared by in situ loading silver nanoparticles (AgNC) on the surface of COFs by sodium borohydride reduction method. AgCOFs are characterized by TEM, SEM, FTIR and XRD. At the same time, the catalytic ability of AgCOFs for trisodium citrate-AgNO3 nanosilver reaction is studied. The results show that AgCOFs can catalyze the reaction of trisodium citrate-AgNO3 to generate silver nanoparticles (AgNPs). The solution color of the system gradually changes from colorless to yellow, and the absorbance value increases. Based on the catalytic reaction of AgCOFs and the regulation effect of nucleic acid aptamer reaction on AgCOFs, a new "on-off-on" colorimetric analysis platform is constructed and applied to the detection of trace Pb2+ in water samples. This analytical platform is simple, sensitive and selective. Finally, the catalytic mechanism of the system is discussed to verify the feasibility of constructing a colorimetric analysis platform.

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.

Predicting hydration layers on surfaces using deep learning

Characterisation of the nanoscale interface formed between minerals and water is essential to the understanding of natural processes, such as biomineralization, and to develop new technologies where function is dominated by the mineral-water interface. Atomic force microscopy offers the potential to characterize solid-liquid interfaces in high-resolution, with several experimental and theoretical studies offering molecular scale resolution by linking measurements directly to water density on the surface. However, the theoretical techniques used to interpret such results are computationally intensive and development of the approach has been limited by interpretation challenges. In this work, we develop a deep learning architecture to learn the solid-liquid interface of polymorphs of calcium carbonate, allowing for the rapid predictions of density profiles with reasonable accuracy.