Material discovery by combining stochastic surface walking global optimization with a neural network

Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia

Cerium-stabilized zirconia (Ce1-xZrxOy, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (Ov) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest Ov diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable Ov pairs along the ⟨110⟩ direction, which reduces the energy barrier of Ov diffusion in the bulk and promotes O2 activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications.

Stabilization of Pd0 by Cu Alloying: Theory-Guided Design of Pd3Cu Electrocatalyst for Anodic Methanol Carbonylation

Electrocatalytic carbonylation of CO and CH3OH to dimethyl carbonate (DMC) on metallic palladium (Pd) electrode offers a promising strategy for C1 valorization at the anode. However, its broader application is limited by the high working potential and the low DMC selectivity accompanied with severe methanol self-oxidation. Herein, our theoretical analysis of the intermediate adsorption interactions on both Pd0 and Pd4+ surfaces revealed that inevitable reconstruction of Pd surface under strongly oxidative potential diminishes its CO adsorption capacity, thus damaging the DMC formation. Further theoretical modeling indicates that doping Pd with Cu not only stabilizes low-valence Pd in oxidative environments but also lowers the overall energy barrier for DMC formation. Guided by this insight, we developed a facile two-step thermal shock method to prepare PdCu alloy electrocatalysts for DMC. Remarkably, the predicted Pd3Cu demonstrated the highest DMC selectivity among existing Pd-based electrocatalysts, reaching a peaked DMC selectivity of 93 % at 1.0 V versus Ag/AgCl electrode. (Quasi) in situ spectra investigations further confirmed the predicted dual role of Cu dopant in promoting Pd-catalyzed DMC formation.

Nature-Inspired Photocatalytic Azo Bond Cleavage with Red Light

The emerging field of photoredox catalysis in mammalian cells enables spatiotemporal regulation of a wealth of biological processes. However, the selective cleavage of stable covalent bonds driven by low-energy visible light remains a great challenge. Herein, we report that red light excitation of a commercially available dye, abbreviated NMB+, leads to catalytic cleavage of stable azo bonds in both aqueous solutions and hypoxic cells and hence a means to photodeliver drugs or functional molecules. Detailed mechanistic studies reveal that azo bond cleavage is triggered by a previously unknown consecutive two-photon process. The first photon generates a triplet excited state, 3NMB+*, that is reductively quenched by an electron donor to generate a protonated NMBH•+. The NMBH•+ undergoes a disproportionation reaction that yields the initial NMB+ and two-electron-reduced NMBH (i.e., leuco-NMB, abbreviated as LNMB). Interestingly, LNMB forms a charge transfer complex with all four azo substrates that possess an intense absorption band in the red region. A second red photon induces electron transfer from LNMB to the azo substrate, resulting in azo bond cleavage. The charge transfer complex mediated two-photon catalytic mechanism reported herein is reminiscent of the flavin-dependent natural photoenzyme that catalyzes bond cleavage reactions with high-energy photons. The red-light-driven photocatalytic strategy offers a new approach to bioorthogonal azo bond cleavage for photodelivery of drugs or functional molecules.

Amorphous Chloride Solid Electrolytes with High Li-Ion Conductivity for Stable Cycling of All-Solid-State High-Nickel Cathodes

Solid electrolytes (SEs) are central components that enable high-performance, all-solid-state lithium batteries (ASSLBs). Amorphous SEs hold great potential for ASSLBs because their grain-boundary-free characteristics facilitate intact solid-solid contact and uniform Li-ion conduction for high-performance cathodes. However, amorphous oxide SEs with limited ionic conductivities and glassy sulfide SEs with narrow electrochemical windows cannot sustain high-nickel cathodes. Herein, we report a class of amorphous Li-Ta-Cl-based chloride SEs possessing high Li-ion conductivity (up to 7.16 mS cm-1) and low Young's modulus (approximately 3 GPa) to enable excellent Li-ion conduction and intact physical contact among rigid components in ASSLBs. We reveal that the amorphous Li-Ta-Cl matrix is composed of LiCl43-, LiCl54-, LiCl65- polyhedra, and TaCl6- octahedra via machine-learning simulation, solid-state 7Li nuclear magnetic resonance, and X-ray absorption analysis. Attractively, our amorphous chloride SEs exhibit excellent compatibility with high-nickel cathodes. We demonstrate that ASSLBs comprising amorphous chloride SEs and high-nickel single-crystal cathodes (LiNi0.88Co0.07Mn0.05O2) exhibit ∼99% capacity retention after 800 cycles at ∼3 C under 1 mA h cm-2 and ∼80% capacity retention after 75 cycles at 0.2 C under a high areal capacity of 5 mA h cm-2. Most importantly, a stable operation of up to 9800 cycles with a capacity retention of ∼77% at a high rate of 3.4 C can be achieved in a freezing environment of -10 °C. Our amorphous chloride SEs will pave the way to realize high-performance high-nickel cathodes for high-energy-density ASSLBs.

The Identity of Nickel Peroxide as a Nickel Superoxyhydroxide for Enhanced Electrocatalysis

Nickel peroxides are a class of stoichiometric oxidants that can selectively oxidize various organic compounds, but their molecular level structure remained elusive until now. Herein, we utilized structural prediction using the Stochastic Surface Walking method based on a neural network potential energy surface and advanced characterization using the as-synthesized nickel peroxide to unravel its chemical identity as the bridging superoxide containing nickel hydroxide, or nickel superoxyhydroxide. Superoxide incorporation tunes the local chemical environment of nickel and oxygen beyond the conventional Bode plot, offering a 6.4-fold increase in the electrocatalytic activity of urea oxidation. A volcanic dependence of the activity on the oxygen equivalents leads to the proposed active site of the Ni(OO)(OH)Ni five-membered ring. This work not only unveils the possible structures of nickel peroxides but also emphasizes the significance of tailoring the oxygen environment for advanced catalysis.

Unravelling the Impact of Metal Dopants and Oxygen Vacancies on Syngas Conversion over Oxides: A Machine Learning-Accelerated Study of CO Activation on Cr-Doped ZnO Surfaces

As a critical component of the OX-ZEO composite catalysts toward syngas conversion, the Cr-doped ZnO ternary system can be considered as a model system for understanding oxide catalysts. However, due to the complexity of its structures, traditional approaches, both experimental and theoretical, encounter significant challenges. Herein, we employ machine learning-accelerated methods, including grand canonical Monte Carlo and genetic algorithm, to explore the ZnO(1010) surface with various Cr and oxygen vacancy (OV) concentrations. Stable surfaces with varied Cr and OV concentrations were then systematically investigated to examine their influence on the CO activation via density functional theory calculations. We observe that Cr tends to preferentially appear on the surface of ZnO(1010) rather than in its interior regions and Cr-doped structures incline to form rectangular islands along the [0001] direction at high Cr and OV concentrations. Additionally, detailed calculations of CO reactivity unveil an inverse relationship between the reaction barrier (Ea) for C-O bond dissociation and the Cr and OV concentrations, and a linear relationship is observed between OV formation energy and Ea for CO activation. Further analyses indicate that the C-O bond dissociation is much more favored when the adjacent OVs are geometrically aligned in the [1210] direction, and Cr is doped around the reactive sites. These findings provide a deeper insight into CO activation over the Cr-doped ZnO surface and offer valuable guidance for the rational design of effective catalysts for syngas conversion.

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

Regulating reconstruction of oxide-derived Cu for electrochemical CO2 reduction toward n-propanol

Oxide-derived copper (OD-Cu) is the most efficient and likely practical electrocatalyst for CO2 reduction toward multicarbon products. However, the inevitable but poorly understood reconstruction from the pristine state to the working state of OD-Cu under strong reduction conditions largely hinders the rational construction of catalysts toward multicarbon products, especially C3 products like n-propanol. Here, we simulate the reconstruction of CuO and Cu2O into their derived Cu by molecular dynamics, revealing that CuO-derived Cu (CuOD-Cu) intrinsically has a richer population of undercoordinated Cu sites and higher surficial Cu atom density than the counterpart Cu2O-derived Cu (Cu2OD-Cu) because of the vigorous oxygen removal. In situ spectroscopes disclose that the coordination number of CuOD-Cu is considerably lower than that of Cu2OD-Cu, enabling the fast kinetics of CO2 reaction and strengthened binding of *C2 intermediate(s). Benefiting from the rich undercoordinated Cu sites, CuOD-Cu achieves remarkable n-propanol faradaic efficiency up to ~17.9%, whereas the Cu2OD-Cu dominantly generates formate.

Aqueous growth of titania subnanoparticles: an understanding of the ultrasmall visible-light-absorbing unit of (TiO2)8(H2O)16

An atomistic understanding of the initial hydrothermal growth of titania remains crucial for the development of nanosized materials, where the presence of water strongly affects the particle growth in comparison to the vapor-phase growth. Herein, we explore the structural evolution of aqueous titania from its salt precursors and determine the nanoparticle configurations in the practical environment by invoking ab initio molecular dynamic simulations and a machine-learning accelerated structural search. Thermodynamically, Ti(OH)4·2H2O serving as the hydrated monomer undergoes planar-to-tubular-to-spherical multistage growth in the Ti(OH)4/H2O hydrothermal system, in which large-sized (TiO2)n(H2O)m particles (n = 1-20) are generated via the olation/oxolation reaction. Importantly, in a mixture of particles of different sizes, we identify that (TiO2)8(H2O)16 is one of the most abundant species in solution with peculiar metastability and exhibits extraordinary visible-light absorption ability, which may be the smallest aqueous titania subnanoparticle in the form of suspension and worth exploring for photocatalytic applications.

An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning

Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.

Silica-water superstructure and one-dimensional superionic conduit in Earth's mantle

Water in Earth's deep interior is predicted to be hydroxyl (OH-) stored in nominally anhydrous minerals, profoundly modulating both structure and dynamics of Earth's mantle. Here, we use a high-dimensional neuro-network potential and machine learning algorithm to investigate the weight percent water incorporation in stishovite, a main constituent of the subducted oceanic crust. We found that stishovite and water prefer forming medium- to long-range ordered superstructures, featuring one-dimensional (1D) water channels. Synthesizing single crystals of hydrous stishovite, we verified the ordering of OH- groups in the water channels through optical and nuclear magnetic resonance spectroscopy and found an average H-H distance of 2.05(3) Å, confirming simulation results. Upon heating, H atoms were predicted to behave fluid-like inside the channels, leading to an exotic 1D superionic state. Water-bearing stishovite could feature high ionic mobility and strong electrical anisotropy, manifesting as electrical heterogeneity in Earth's mantle.

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.

Deep reaction network exploration of glucose pyrolysis

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple [Formula: see text]-D-glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state-guided reaction exploration has been performed that provides complete pathways to most significant experimental pyrolysis products of [Formula: see text]-D-glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semiempirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized with density function theory, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of the Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)identified pathways to most major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yields in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.

In Situ Structure of a Mo-Doped Pt-Ni Catalyst during Electrochemical Oxygen Reduction Resolved from Machine Learning-Based Grand Canonical Global Optimization

Pt-Ni alloy is by far the most active cathode material for oxygen reduction reaction (ORR) in the proton-exchange membrane fuel cell, and the addition of a tiny amount of a third-metal Mo can significantly improve the catalyst durability and activity. Here, by developing machine learning-based grand canonical global optimization, we are able to resolve the in situ structures of this important three-element alloy system under ORR conditions and identify their correlations with the enhanced ORR performance. We disclose the bulk phase diagram of Pt-Ni-Mo alloys and determine the surface structures under the ORR reaction conditions by exploring millions of likely structure candidates. The pristine Pt-Ni-Mo alloy surfaces are shown to undergo significant structure reconstruction under ORR reaction conditions, where a surface-adsorbed MoO₄ monomer or Mo₂O _x dimers cover the Pt-skin surface above 0.9 V vs RHE and protect the surface from Ni leaching. The physical origins are revealed by analyzing the electronic structure of O atoms in MoO₄ and on the Pt surface. In viewing the role of high-valence transition metal oxide clusters, we propose a set of quantitative measures for designing better catalysts and predict that six elements in the periodic table, namely, Mo, Tc, Os, Ta, Re, and W, can be good candidates for alloying with PtNi to improve the ORR catalytic performance. We demonstrate that machine learning-based grand canonical global optimization is a powerful and generic tool to reveal the catalyst dynamics behavior in contact with a complex reaction environment.

Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2 Activation Using Machine Learning-Accelerated First-Principles Simulations

Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101̅0) for CO and H₂ activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101̅0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H₂:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn₃ cluster site, allowing H₂ homolysis and C-O bond cleavage to occur. Furthermore, the activity of intrinsic sites (Zn_3c and O_3c sites) is almost invariable, while the activity of the generated OV sites is strongly dependent on the concentration of the OVs. It is also found that OV distributions on the surface can considerably affect the reactions; the barrier of C-O bond dissociation is significantly reduced when the OVs are aligned along the [12̅10] direction. These findings may be general in the systems with metal oxides in heterogeneous catalysis and may have significant impacts on the field of catalyst design by regulating the concentration and distribution of the OVs.

Pd/Xu-Phos-catalyzed asymmetric elimination of fully substituted enol triflates into axially chiral trisubstituted allenes

The β-H elimination, as one of the most important elementary reactions in transition metal chemistry, is a key step in quenching the carbon-palladium bond for the Heck reaction. However, the β-H elimination of the alkenyl palladium species leading to allene is an energetically unfavored process, and therefore, it has been a long-standing challenge in control of this process via enantioselective manner. We developed a concise and efficient methodology to construct trisubstituted chiral allenes from stereodefined fully substituted enol triflates by the enantioselective β-H elimination of the alkenyl palladium species under mild conditions. The identified Xu-Phos play a crucial role in the chemoselectivity and enantioselectivity. Multiple linear regression analysis shows the important steric effect on enantioselectivity. DFT computation results allow us to propose an intramolecular base (^-OAc)-assisted deprotonation mechanism for this progress. Distortion-interaction and energy decomposition analysis indicate that the difference in electrostatic energy (E_elec) of the two intramolecular base-assisted deprotonation transition states dominates the stereoselectivity.

Nature of Catalytic Behavior of Cobalt Oxides for CO2 Hydrogenation

Cobalt oxide (CoO _x ) catalysts are widely applied in CO₂ hydrogenation but suffer from structural evolution during the reaction. This paper describes the complicated structure-performance relationship under reaction conditions. An iterative approach was employed to simulate the reduction process with the help of neural network potential-accelerated molecular dynamics. Based on the reduced models of catalysts, a combined theoretical and experimental study has discovered that CoO(111) provides active sites to break C-O bonds for CH₄ production. The analysis of the reaction mechanism indicated that the C-O bond scission of *CH₂O species plays a key role in producing CH₄. The nature of dissociating C-O bonds is attributed to the stabilization of *O atoms after C-O bond cleavage and the weakening of C-O bond strength by surface-transferred electrons. This work may offer a paradigm to explore the origin of performance over metal oxides in heterogeneous catalysis.

Long-range ordered porous carbons produced from C60

Carbon structures with covalent bonds connecting C₆₀ molecules have been reported^1-3, but their production methods typically result in very small amounts of sample, which restrict the detailed characterization and exploration necessary for potential applications. We report the gram-scale preparation of a new type of carbon, long-range ordered porous carbon (LOPC), from C₆₀ powder catalysed by α-Li₃N at ambient pressure. LOPC consists of connected broken C₆₀ cages that maintain long-range periodicity, and has been characterized by X-ray diffraction, Raman spectroscopy, magic-angle spinning solid-state nuclear magnetic resonance spectroscopy, aberration-corrected transmission electron microscopy and neutron scattering. Numerical simulations based on a neural network show that LOPC is a metastable structure produced during the transformation from fullerene-type to graphene-type carbons. At a lower temperature, shorter annealing time or by using less α-Li₃N, a well-known polymerized C₆₀ crystal forms owing to the electron transfer from α-Li₃N to C₆₀. The carbon K-edge near-edge X-ray absorption fine structure shows a higher degree of delocalization of electrons in LOPC than in C₆₀(s). The electrical conductivity is 1.17 × 10^-2 S cm^-1 at room temperature, and conduction at T < 30  K appears to result from a combination of metallic-like transport over short distances punctuated by carrier hopping. The preparation of LOPC enables the discovery of other crystalline carbons starting from C₆₀(s).

Atomic-Scale Mechanism of Platinum Catalyst Restructuring under a Pressure of Reactant Gas

Heterogeneous catalysis is key for chemical transformations. Understanding how catalysts' active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurately determining catalytic mechanisms and predictably developing catalysts. We combine in situ time-dependent scanning tunneling microscopy observations and machine-learning-accelerated first-principles atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create sub-nano-islands on the terraces, where under-coordinated sites are stabilized by the CO adsorbates. The fast and accurate machine-learning potential is key to enabling the exploration of tens of thousands of configurations for the CO-covered restructuring catalyst. These studies open an avenue to achieve an atomic-scale understanding of the structural dynamics of more complex metal nanoparticle catalysts under reaction conditions.

Neural Network Accelerated Investigation of the Dynamic Structure-Performance Relations of Electrochemical CO2 Reduction over SnO x Surfaces

Heterogeneous catalysts, especially metal oxides, play a curial role in improving energy conversion efficiency and production of valuable chemicals. However, the surface structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. This paper describes a strategy of the multiscale simulation to investigate the SnO _x reduction process and to build a structure-performance relation of SnO _x for CO₂ electroreduction. Employing high-dimensional neural network potential accelerated molecular dynamics and stochastic surface walking global optimization, coupled with density functional theory calculations, we propose that SnO₂ reduction is accompanied by surface reconstruction and charge density redistribution of active sites. A regulatory factor, the net charge, is identified to predict the adsorption capability for key intermediates on active sites. Systematic electronic analyses reveal the origin of the interaction between the adsorbates and the active sites. These findings uncover the quantitative correlation between electronic structure properties and the catalytic performance of SnO _x so that Sn sites with moderate charge could achieve the optimally catalytic performance of the CO₂ electroreduction to formate.

MnO2-based materials for supercapacitor electrodes: challenges, strategies and prospects

Manganese dioxide (MnO₂) has always been the ideal electrode material for supercapacitors due to its non-toxic nature and high theoretical capacity (1370 F g^-1). Over the past few years, significant progress has been made in the development of high performance MnO₂-based electrode materials. This review summarizes recent research progress in experimental, simulation and theoretical studies for the modification of MnO₂-based electrode materials from different perspectives of morphology engineering, defect engineering and heterojunction engineering. Several main approaches to achieve enhanced electrochemical performance are summarized, respectively increasing the effective active site, intrinsic conductivity and structural stability. On this basis, the future problems and research directions of electrode materials are further envisaged, which provide theoretical guidance for the adequate design and synthesis of MnO₂-based electrode materials for use in supercapacitors.

Application of machine-learning-based global optimization: potential-dependent co-electrosorbed structure and activity on the Pd(110) surface

Electrodes can adsorb different reaction intermediates under electrochemical conditions, which in turn significantly affect their electrochemical performance. This complex phenomenon attracts continuous interest in both science and industry for understanding the co-electrosorbed structure and activity under electrochemical conditions. Here, we report the first theoretical attempt by combining the machine-learning-based global optimization (SSW-NN method) and modified Poisson-Boltzmann continuum solvation (CM-MPB) based on first-principles calculations to elucidate the potential-dependent co-electrosorbed species on the Pd(110) surface. We reveal the potential-dependence adsorption/absorption hydrogen phases, the phase transition of α-H_ri/Pd to β-H_ri/Pd, and the co-electrosorbed H_ri-NH_y surface structures. In particular, we found that H_ri-NH₂ and H_ri-NH₃ are favorable intermediates for the N₂ reduction reaction, and the subsurface H is the key species responsible for NH₂ hydrogenation on the Pd(110) electrode.

Methanol Synthesis from CO2/CO Mixture on Cu-Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

Methanol synthesis on industrial Cu/ZnO/Al₂O₃ catalysts via the hydrogenation of CO and CO₂ mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO₂ in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO₂ and CO hydrogenations on thermodynamically favorable Cu-Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn-Zn dimeric sites being avoided. CO₂ and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO₂ hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [-Zn-OH-Zn-] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system.

Smallest Stable Si/SiO_{2} Interface that Suppresses Quantum Tunneling from Machine-Learning-Based Global Search

While the downscaling of size for field effect transistors is highly desirable for computation efficiency, quantum tunneling at the Si/SiO_{2} interface becomes the leading concern when approaching the nanometer scale. By developing a machine-learning-based global search method, we now reveal all the likely Si/SiO_{2} interface structures from thousands of candidates. Two high Miller index Si(210) and (211) interfaces, being only ∼1  nm in periodicity, are found to possess good carrier mobility, low carrier trapping, and low interfacial energy. The results provide the basis for fabricating stepped Si surfaces for next-generation transistors.

Zeolite-confined subnanometric PtSn mimicking mortise-and-tenon joinery for catalytic propane dehydrogenation

Heterogeneous catalysts are often composite materials synthesized via several steps of chemical transformation, and thus the atomic structure in composite is a black-box. Herein with machine-learning-based atomic simulation we explore millions of structures for MFI zeolite encapsulated PtSn catalyst, demonstrating that the machine-learning enhanced large-scale potential energy surface scan offers a unique route to connect the thermodynamics and kinetics within catalysts' preparation procedure. The functionalities of the two stages in catalyst preparation are now clarified, namely, the oxidative clustering and the reductive transformation, which form separated Sn₄O₄ and PtSn alloy clusters in MFI. These confined clusters have high thermal stability at the intersection voids of MFI because of the formation of "Mortise-and-tenon Joinery". Among, the PtSn clusters with high Pt:Sn ratios (>1:1) are active for propane dehydrogenation to propene, ∼10³ in turnover-of-frequency greater than conventional Pt₃Sn metal. Key recipes to optimize zeolite-confined metal catalysts are predicted.

In Situ Active Site for Fe-Catalyzed Fischer-Tropsch Synthesis: Recent Progress and Future Challenges

Fischer-Tropsch synthesis (FTS) that converts syngas into long-chain hydrocarbons is a key technology in the chemical industry. As one of the best catalysts for FTS, the Fe-based composite develops rich solid phases (metal, oxides, and carbides) in the catalytic reaction, which triggered the quest for the true active site in catalysis in the past century. Recent years have seen great advances in probing the active-site structure using modern experimental and theoretical tools. This Perspective serves to highlight these latest achievements, focusing on the geometrical structure and thermodynamic stability of Fe carbide bulk phases, the exposed surfaces, and their relationship to FTS activity. The current reaction mechanisms on CO activation and carbon chain growth are also discussed, in the context of theoretical models and experimental evidence. We also present the outlook regarding the current challenges in Fe-based FTS.

Topological Ordering of Memory Glass on Extended Length Scales

Identifying ordering in non-crystalline solids has been a focus of natural science since the publication of Zachariasen's random network theory in 1932, but it still remains as a great challenge of the century. Literature shows that the hierarchical structures, from the short-range order of first-shell polyhedra to the long-range order of translational periodicity, may survive after amorphization. Here, in a piece of AlPO₄, or berlinite, we combine X-ray diffraction and stochastic free-energy surface simulations to study its phase transition and structural ordering under pressure. From reversible single crystals to amorphous transitions, we now present an unambiguous view of the topological ordering in the amorphous phase, consisting of a swarm of Carpenter low-symmetry phases with the same topological linkage, trapped in a metastable intermediate stage. We propose that the remaining topological ordering is the origin of the switchable "memory glass" effect. Such topological ordering may hide in many amorphous materials through disordered short atomic displacements.

Structure-Function Correlation and Dynamic Restructuring of Cu for Highly Efficient Electrochemical CO2 Conversion

The increasing global demand for sustainable energy sources and emerging environmental issues have pushed the development of energy conversion and storage technologies to the forefront of chemical research. Electrochemical carbon dioxide (CO₂ ) conversion provides an attractive approach to synthesizing fuels and chemical feedstocks using renewable energy. On the path to deploying this technology, basic and applied scientific hurdles remain. Copper, as the only metal catalyst that is capable to produce C₂₊ fuels from CO₂ reduction (CO₂ R), still faces challenges in the improvement of electrosynthesis pathways for highly selective fuel production. In this regard, mechanistically understanding CO₂ R on Cu-based electrocatalysts, particularly identifying the structure-function correlation, is crucial. Here, a broad view of the variable structural parameters and their complex interplay in CO₂ R catalysis on Cu was given, with the purpose of providing deep insights and guiding the future rational design of CO₂ R electrocatalysts. First, this Review described the progress and recent advances in the development of well-defined nanostructured catalysts and the mechanistic understanding on the influences from a particular structure of a catalyst, such as facet, defects, morphology, oxidation state, composition, and interface. Next, the in-situ dynamic restructuring of Cu was presented. The importance of operando characterization methods to understand the catalyst structure-sensitivity was also discussed. Finally, some perspectives on the future outlook for electrochemical CO₂ R were offered.

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li₂ZrCl₆ and Li₂HfCl₆, for stable ASSLMBs. The predicted halide SEs possess high Li⁺ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm²), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g^-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).

Pressure tuned incommensurability and guest structure transition in compressed scandium from machine learning atomic simulation

Scandium (Sc) is the lightest non-main-group element and transforms to a host-guest (H-G) incommensurate structure under gigapascal (GPa) pressures. While the host structure is stable over a wide pressure range, the guest structure may exist in multiple forms, featuring different incommensurate ratios, and mixing up to generate long-range "disordered" guest structures. Here, we employed the recently developed global neural network (g-NN) potential and the stochastic surface walking (SSW) global optimization algorithm to explore the global potential energy surface of Sc under various pressures. We probe the global minima structure in a system made of hundreds of atoms and revealed that the solid-phase transition between Sc-I and H-G Sc-II phases is fully reconstructive in nature. Above 62.5 GPa, the pressure will further destabilize the face-centered tetragonal (fct, Sc-IIa) guest structure to a body-centered tetragonal phase (bct, Sc-IIb), while sustaining the host structure. The structural transition mechanism of this work will shed light on the nature of the complex H-G structural modifications in compressed metals.

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning

The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.

Exploring the physical origin of the electrocatalytic performance of an amorphous alloy catalyst via machine learning accelerated DFT study

The amorphous alloy Pd₄₀Ni₁₀Cu₃₀P₂₀ is a rising star as an HER catalyst since it possesses an excellent electrocatalytic activity and a high durability in practical experiments. However, the physical origin of the electrocatalytic performance of the amorphous alloy catalyst is still unclear due to the difficulty of amorphous modelling and the huge cost of DFT calculations. Here, we built a Smooth Overlap of Atomic Positions-Machine Learning (SOAP-ML) model to accelerate the DFT study on the effect of the local atomic environment of the Pd₄₀Ni₁₀Cu₃₀P₂₀ catalyst. Compared to pure DFT-calculated results and experiment, our model makes a good prediction (MSE = 0.018) of the local atomic environment with the best catalysis. We calculated 40 000 active sites on the amorphous alloy surface and obtained the optimal atomic ratio of the alloy catalyst (Pd : Cu : P : Ni = 0.51 : 0.33 : 0.09 : 0.07), indicating that the Pd d electrons mainly enhance the catalytic performance. We employed the SOAP-ML model to reveal the physical origin of the long durability as the dealloying of Ni, which is highly consistent with the experimental results. The above results all prove the high accuracy and reliability of the established SOAP-ML model and provide an appealing idea for the future application of the amorphous alloy.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Confronting the Air Instability of Cesium Tin Halide Perovskites by Metal Ion Incorporation

Tin halide perovskite's potential as a photovoltaic absorber has not been fully realized to date, largely due to its instability in ambient air. Here, we demonstrate by both experiments and simulations that the air instability of black-phase cesium tin iodide perovskite (γ-CsSnI₃) could be greatly lessened by a controlled incorporation of bismuth (Bi) ions into the crystal lattice. Hall effect measurements on films of γ-CsSnI₃ suggest the unwanted formation of a tin vacancy and p-type self-doping can be effectively suppressed by the Bi incorporation. Structural and optical results indicate that the Bi incorporation markedly enhances the air stability by impeding the direct conversion of γ-CsSnI₃ to zero-dimensional Cs₂SnI₆. By using a stochastic surface walking (SSW) method integrating neural network (NN) potential and density functional theory (DFT), it is revealed that the remarkable enhanced stability could be attributed to a combination of factors originating from lattice-contraction-induced strain, a suppressed tin vacancy, and an increased energy barrier for the transformation of γ-CsSnI₃ to Cs₂SnI₆. This study provides physical insights into the stabilization mechanism of tin perovskites by heterovalent B-site engineering, paving the way for realizing stable and efficient lead-free perovskite photovoltaics.

A reaction route network for methanol decomposition on a Pt(111) surface

A reaction route network for the decomposition reaction of methanol on a Pt(111) surface was constructed by using the artificial force-induced reaction (AFIR) method, which can search for reaction paths automatically and systematically. Then, the network was kinetically analyzed by applying the rate constant matrix contraction (RCMC) method. Specifically, the time hierarchy of the network, the time evolution of the population initially given to CH₃ OH to the other species on the network, and the most favorable route from CH₃ OH to major and minor products were investigated by the RCMC method. Consistently to previous studies, the major product on the network was CO+4H, and the most favorable route proceeded through the following steps: CH₃ OH → CH₂ OH+H → HCOH+2H → HCO+3H → CO+4H. Furthermore, paths to byproducts found on the network and their kinetic importance were discussed. The present procedure combining AFIR and RCMC was thus successful in explaining the title reaction without using any information on its product or the reaction mechanism.

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT

To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H_ad/H₂O_ad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt.

Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality

Recent advances in machine-learned interatomic potentials largely benefit from the atomistic representation and locally invariant many-body descriptors. It was, however, recently argued that including three-body (or even four-body) features is incomplete to distinguish specific local structures. Utilizing an embedded density descriptor made by linear combinations of neighboring atomic orbitals and realizing that each orbital coefficient physically depends on its own local environment, we propose a recursively embedded atom neural network model. We formally prove that this model can efficiently incorporate complete many-body correlations without explicitly computing high-order terms. This model not only successfully addresses challenges regarding local completeness and nonlocality in representative systems, but also provides an easy and general way to update local many-body descriptors to have a message-passing form without changing their basic structures.

Facile Removal of Bulk Oxygen Vacancy Defects in Metal Oxides Driven by Hydrogen-Dopant Evaporation

Oxygen vacancy is a common defect in metal oxides that causes appreciable damage to material properties and performance. Removing bulk defects of oxygen vacancy (V_O) typically needs harsh conditions such as high-temperature annealing. Supported by first-principles simulations, we propose an effective strategy of removing V_O bulk defects in metal oxides by evaporating hydrogen dopants. The hydrogen dopants not only lower the migration barrier of V_O but also push V_O away due to their repulsive interaction. The coevaporation mechanism was supported by a neural networks potential-based molecular dynamics simulation, which shows that the migration of hydrogen dopants from inside to surface at 400 K promotes the migration of V_O as well. Our proof-of-concept study suggests an alternative and efficient way of modulating oxygen vacancies in metal oxides via reversible hydrogen doping.