Identifying Metallic Transition-Metal Dichalcogenides for Hydrogen Evolution through Multilevel High-Throughput Calculations and Machine Learning

Atomic-Level Engineering of Transition Metal Dichalcogenides for Enhanced Hydrogen Evolution Reaction

2D transition metal dichalcogenides (2D-TMDs) have attracted considerable attention due to their characteristic layered structures, which provide abundant accessible surface sites. Significant research efforts are dedicated to designing nanostructures and regulating electron properties to enhance the catalytic performance of the hydrogen evolution reaction (HER) of TMDs. However, elucidating the HER mechanism, particularly the role of active sites, remains challenging owing to the complex surface and electronic structures introduced by nanoscale modification. Recent advances have focused on achieving efficient HER catalysis through atomic-level control of TMD surface structures and precise identification of the coordination environment of active sites. Atomic-level engineering of TMDs, including incorporating or removing specific atoms onto the basal surfaces or within the interlayer via advanced synthetic approaches, has emerged as a promising strategy. These modifications optimize the adsorption/desorption energy of H, increase the density of active sites, and create synergetic active sites by arranging atoms in controlled configuration, in single-atomic modified TMDs (SA-TMDs) catalysts. Further, the insights of a notable increase of HER performance in SA-TMDs are discussed in detail when compared to both their pure and conventionally doped counterparts. This review aims to advance the understanding of atomic-level catalysis and provides a basis for developing next-generation materials for energy applications.

Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

Hydrogen as an environmentally friendly energy carrier, has many significant advantages, such as cleanliness, recyclability, and high calorific value of combustion, which makes it one of the major potential sources of energy supply in the future. Hydrogen evolution reaction (HER) is an important strategy to cope with the global energy shortage and environmental degradation, and given the large cost involved in HER, it is crucial to screen and develop stable and efficient catalysts. Compared with the traditional catalyst development model, the rapid development of data science and technology, especially machine learning technology, has shown great potential in the field of catalyst development in recent years. Among them, the research method of combining high-throughput computing and machine learning has received extensive attention in the field of materials science. Therefore, this paper provides a review of the recent research on combining high-throughput computing with machine learning to guide the development of HER electrocatalysts, covering the application of machine learning in constructing prediction models and extracting key features of catalytic activity. The future challenges and development directions of this field are also prospected, aiming to provide useful references and lessons for related research.

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.

Platinum monolayer dispersed on MXenes for electrocatalyzed hydrogen evolution: a first-principles study

Maximizing platinum's atomic utilization and understanding the anchoring mechanism between platinum moieties and their supports are crucial for the hydrogen evolution reaction (HER). Using density functional theory, we investigate the catalyst of a Pt monolayer on the two-dimensional Mo2TiC2 substrate (PtML/Mo2TiC2) for the reaction. This Pt monolayer shows a Pt(111)-like pattern, with its Pt-Pt bond elongated by about 0.1 Å compared to Pt(111); charge transfer from Mo2TiC2 to the Pt monolayer leads to significant charge accumulation on Pt. This substantial monolayer metal-support interaction optimizes hydrogen adsorption toward optimal HER activity under both constant charge and potential conditions, making PtML/Mo2TiC2 a promising HER catalyst. Detailed studies reveal that the dominant Volmer-Tafel mechanism in the HER occurs on the 1 monolayer hydrogen-covered PtML/Mo2TiC2 surface. The surface Pourbaix diagram identifies this as the stable surface termination under the electrochemical reaction conditions. These findings provide insights into designing stable, efficient, and low platinum-loaded HER catalysts.

Interfacial Electron Potential Well Facilitates the Design of Cobalt Phosphide Heterojunctions for Hydrogen Evolution

The interfacial electron modulation of electrocatalysts is an effective way to realize efficient hydrogen production, which is of great importance for future renewable energy systems. However, systematic theory-guided design of catalysts in heterojunction coupling is lacking. In this work, a multi-level theoretical calculation is performed to screen optimal candidates to form a heterojunction with CoP (101) surface for electrocatalytic hydrogen production. To overcome the weak adsorption of H+ on CoP (101), rational design of electrons potential well at the heterojunction interface can effectively enhance the hydrogen adsorption. All p-type cobalt-based phosphides are considered potential candidates at the beginning. After screening for conductivity, stability, interface matching screening, and ΔGH* evaluation, the CoP/Co2P-H system is identified to be able to display optimal hydrogen production performance. To verify the theoretical design, CoP, CoP/Co2P-H, and CoP/Co2P-O are synthesized and the electrochemical analysis is carried out. The hydrogen evolution reaction (HER) performance is consistent with the prediction. This work utilizes the electron potential well effect and multi-level screening calculations to design highly efficient heterojunction catalysts, which can provide useful theoretical guidance for the rational design of heterojunction-type catalysts.

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

Computational screening of layered metal chalcogenide materials for HER electrocatalysts, and its synergy with experiments

Layered materials have emerged as attractive candidates in our search for abundant, inexpensive and efficient hydrogen evolution reaction (HER) catalysts, due to larger specific area these offer. Among these, transition metal dichalcogenides have been studied extensively, while ternary transition metal tri-chalcogenides have emerged as promising candidates recently. Computational screening has emerged as a powerful tool to identify the promising materials out of an initial set for specific applications, and has been employed for identifying HER catalysts also. This article presents a comprehensive review of how computational screening studies based on density functional calculations have successfully identified the promising materials among the layered transition metal di- and tri-chalcogenides. Synergy of these computational studies with experiments is also reviewed. It is argued that experimental verification of the materials, predicted to be efficient catalysts but not yet tested, will enlarge the list of materials that hold promise to replace expensive platinum, and will help ushering in the much awaited hydrogen economy.

Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction

Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) heterostructures to unravel the rich landscape of possibilities for enhancing the hydrogen evolution reaction (HER) activity. The stability of the heterostructures and the intercalation within the substrates are verified through adhesion and binding energies, showcasing the significant impact of chalcogenide selection on the interaction properties. Based on hydrogen adsorption Gibbs free energy (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as a robust tool in predicting HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby paving the way for accelerated catalyst screening. The Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and intercalation of TM atoms at the N site (IN). Overall, our integrated approach utilizing DFT and ML effectively identifies hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity in heterostructures intercalated with Sc and Ti, underscoring their potential for advancing catalytic performance.

Genetic descriptor search algorithm for predicting hydrogen adsorption free energy of 2D material

Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ([Formula: see text]) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications.

Data-Driven Discovery of Graphene-Based Dual-Atom Catalysts for Hydrogen Evolution Reaction with Graph Neural Network and DFT Calculations

The flexible tuning ability of dual-atom catalysts (DACs) makes them an ideal system for a wide range of electrochemical applications. However, the large design space of DACs and the complexity in the binding motif of electrochemical intermediates hinder the efficient determination of DAC combinations for desirable catalytic properties. A crystal graph convolutional neural network (CGCNN) was adopted for DACs to accelerate the high-throughput screening of hydrogen evolution reaction (HER) catalysts. From a pool of 435 dual-atom combinations in N-doped graphene (N₆Gr), we screened out two high-performance HER catalysts (AuCo@N₆Gr and NiNi@N₆Gr) with excellent HER, electronic conductivity, and stability using the combination of CGCNN and density functional theory (DFT). Furthermore, comprehensive DFT studies were conducted on these two catalysts to confirm their outstanding reaction kinetics and to understand the cooperative effect between the metal pair for HER. To obtain ideal hydrogen binding in AuCo, the inert Au weakens the strong hydrogen binding of Co, while for NiNi, the two weakly binding Ni cooperate. The present protocol was able to select the two catalysts with different physical origins for HER and can be applied to other DAC catalysts, which should hasten catalyst discovery.

Ab initio calculation of the adsorption of As, Cd, Cr, and Hg heavy metal atoms onto the illite(001) surface: Implications for soil pollution and reclamation

Elucidating the mechanisms of heavy metal (HM) adsorption on clay minerals is key to solving HM pollution in soil. In this study, the adsorption of four HM atoms (As, Cd, Cr, and Hg) on the illite(001) surface was investigated using density functional theory calculations. Different adsorption configurations were investigated and the electronic properties (i.e., adsorption energy (E_ad) and electron transfer) were analyzed. The E_ad values of the four HM atoms on the illite(001) surface were found to be As > Cr > Cd > Hg. The E_ad values for the most stable adsorption configurations of As, Cr, Cd, and Hg were -1.8554, -0.7982, -0.3358, and -0.2678 eV, respectively. The As atoms show effective chemisorption at all six adsorption sites, while Cd, Cr, and Hg atoms mainly exhibited physisorption. The hollow and top (O) sites were more favorable than the top (K) sites for the adsorption of HM atoms. The Gibbs free energy results show that the illite(001) surface was energetically favorable for the adsorption of As and Cr atoms under the influence of 298 K and 1 atm. After adsorption, there was a redistribution of positions and reconfiguration of the chemical bonding of the surface atoms, with a non-negligible influence around the upper surface atoms. Bader charge analysis shows electrons were transferred from the surface to the HM atoms, and a strong correlation between the valence electron variations and the adsorption energy was observed. HM atoms had a high electronic state overlap with the surface O atoms near the Fermi energy level, indicating that the surface O atoms, though not the topmost atoms around the surface, significantly influence HM adsorption. The above results show illite(001) preferentially adsorbed As among all four investigated HM atoms, indicating that soils containing a high proportion of illite might be more prone to As pollution.

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.

Strategies to improve electrocatalytic performance of MoS2-based catalysts for hydrogen evolution reactions

Electrocatalytic hydrogen evolution reactions (HERs) are a key process for hydrogen production for clean energy applications. HERs have unique advantages in terms of energy efficiency and product separation compared to other methods. Molybdenum disulfide (MoS2) has attracted extensive attention as a potential HER catalyst because of its high electrocatalytic activity. However, the HER performance of MoS2 needs to be improved to make it competitive with conventional Pt-based catalysts. Herein, we summarize three typical strategies for promoting the HER performance, i.e., defect engineering, heterostructure formation, and heteroatom doping. We also summarize the computational density functional theory (DFT) methods used to obtain insight that can guide the construction of MoS2-based materials. Additionally, the challenges and prospects of MoS2-based catalysts for the HER have also been discussed.

The effect of heteroatom doping on the active metal site of CoS2 for hydrogen evolution reaction

The exploration of cost-effective hydrogen evolution reaction (HER) electrocatalysts through water splitting is important for developing clean energy technology and devices. The application of CoS2 in HER has been drawing more and more attention due to its low cost and relatively satisfactory HER catalytic performance. And CoS2 was found to exhibit excellent HER catalytic performance after appropriate doping according to other experimental investigations. However, the theoretical simulation and the intrinsic catalytic mechanism of CoS2 remains insufficiently investigated. Therefore, in this study, density functional theory is used to investigate the HER catalytic activity of CoS2 doped with a heteroatom. The results show that Pt-, N- and O-doped CoS2 demonstrates smaller Gibbs free energies close to that of Pt, compared with the original CoS2 and CoS2 doped with other atoms. Furthermore, HER catalytic performance of CoS2 can be improved by tuning d-band centers of H adsorption sites. This study provides an effective method to achieve modified CoS2 for high-performance HER and to investigate other transition metal sulfides as HER electrode.

Engineering Phase Stability of Semimetallic MoS2 Monolayers for Sustainable Electrocatalytic Hydrogen Production

1T'-phase MoS₂ possesses excellent electrocatalytic performance, but due to the instability of the thermodynamic metastable phase, its actual electrocatalytic effect is seriously limited. Here, we report a wet-chemical synthesis strategy for constructing rGO/1T'-MoS₂/CeO₂ heterostructures to improve the phase stability of metastable 1T' phase MoS₂ monolayers. Importantly, the rGO/1T'-MoS₂/CeO₂ heterostructure exhibits excellent electrocatalytic hydrogen evolution reaction (HER) performance, which is much better than the 1T'-MoS₂ monolayers. The synergistic effects between CeO₂ nanoparticles (NPs) and 1T'-MoS₂ monolayers were systematically investigated. 1T'-MoS₂ monolayers combined with the cocatalyst of CeO₂ NPs can produce lattice strain and distortion on 1T'-MoS₂ monolayers, which can tune the energy band structure, charge transfer, and energy barriers of hydrogen atom adsorption (ΔE_H), leading to promotion of the phase activity and stability of 1T'-MoS₂ monolayers for hydrogen production. Our work offers a feasible method for the preparation of efficient HER electrocatalysts based on the engineering phase stability of metastable materials.

General Bottom-Up Colloidal Synthesis of Nano-Monolayer Transition-Metal Dichalcogenides with High 1T'-Phase Purity

Phase engineering of nanomaterials provides a promising way to explore the phase-dependent physicochemical properties and various applications of nanomaterials. A general bottom-up synthesis method under mild conditions has always been challenging globally for the preparation of the semimetallic phase-transition-metal dichalcogenide (1T'-TMD) monolayers, which are pursued owing to their unique electrochemical property, unavailable in their semiconducting 2H phases. Here, we report the general scalable colloidal synthesis of nanosized 1T'-TMD monolayers, including 1T'-MoS₂, 1T'-MoSe₂, 1T'-WS₂, and 1T'-WSe₂, which are revealed to be of high phase purity. Moreover, the surfactant-reliant stacking-hinderable growth mechanism of 1T'-TMD nano-monolayers was unveiled through systematic experiments and theoretical calculations. As a proof-of-concept application, the 1T'-TMD nano-monolayers are used for electrocatalytic hydrogen production in an acidic medium. The 1T'-MoS₂ nano-monolayers possess abundant in-plane electrocatalytic active sites and high conductivity, coupled with the contribution of the lattice strain, thus exhibiting excellent performance. Importantly, the catalyst shows impressive endurability in electroactivity. Our developed general scalable strategy could pave the way to extend the synthesis of other broad metastable semimetallic-phase TMDs, which offer great potential to explore novel crystal phase-dependent properties with wide application development for catalysis and beyond.

Catalytic Activity Trends of Pyrite Transition Metal Dichalcogenides for Oxygen Reduction and Evolution

Transition metal dichalcogenides (TMDs) have been considered as promising materials for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysis. While there have been numerous studies focusing on layered...

Unveiling the Mechanisms Ruling the Efficient Hydrogen Evolution Reaction with Mitrofanovite Pt3Te4

By means of electrocatalytic tests, surface-science techniques and density functional theory, we unveil the physicochemical mechanisms ruling the electrocatalytic activity of recently discovered mitrofanovite (Pt₃Te₄) mineral. Mitrofanovite represents a very promising electrocatalyst candidate for energy-related applications, with a reduction of costs by 47% compared to pure Pt and superior robustness to CO poisoning. We show that Pt₃Te₄ is a weak topological metal with the Z2 invariant, exhibiting electrical conductivity (∼4 × 10⁶ S/m) comparable with pure Pt. In hydrogen evolution reaction (HER), the electrode based on bulk Pt₃Te₄ shows a very small overpotential of 46 mV at 10 mA cm^-2 and a Tafel slope of 36-49 mV dec^-1 associated with the Volmer-Heyrovsky mechanism. The outstanding ambient stability of Pt₃Te₄ also provides durability of the electrode and long-term stability of its efficient catalytic performances.

Computational investigation of two-dimensional borides with planar octacoordinated main group elements

In this communication, by means of first principles calculations, we searched stable two-dimensional borides with octacoordinated main group elements, and we found that only AlB4 monolayer is stable in the planar octacoordinate motif through our stability screenings, which can be used for electrocatalyzing the hydrogen evolution reaction.

Single-atom catalysts based on TiN for the electrocatalytic hydrogen evolution reaction: a theoretical study

The electrocatalytic hydrogen evolution reaction (HER) for water splitting is crucial for the sustainable production of clean hydrogen fuel, while the high cost of Pt catalysts impedes its commercialization. Herein, we have performed a systematic theoretical study on the electrocatalytic HER over single-atom catalysts (SACs) based on low-cost TiN. Specifically, the TiN(100) surface with a Ti or N vacancy has been considered as the support. 20 transition-metal (TM) atoms and 3 nonmetallic atoms are embedded into the Ti or N vacancy, accordingly denoted as M@Tiv or M@Nv. All the single atoms can be stabilized by the surface vacancies, controlled by the adjustable chemical potential. Interestingly, for TM-embedded TiN(100), the hydrogen binding is much stronger over M@Nv than M@Tiv, which can be attributed to the more localized d states of the TM atoms anchored by the N vacancies, indicating a strong coordination effect. Among 43 catalysts, 10 (Ni, Zn, Nb, Mo, Rh@Tiv, and Au, Pd, W, Mo, B@Nv) were predicted to have high HER catalytic activity with near-zero hydrogen adsorption free energy. For the further gaseous hydrogen evolution, Zn@Tiv can adopt both Tafel (with an energy barrier of 0.68 eV) and Heyrovsky mechanisms, while the others may prefer the Heyrovsky mechanism. This work provides a promising strategy to realize cost-efficient electrocatalysts for the HER, and highlights the important role of the local coordination environment for SACs.