In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C2 Selectivity over MnRh Binary Catalysts.

Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.

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.

Selective Cleavage of α-Olefins to Produce Acetylene and Hydrogen.

Acetylene production from mixed α-olefins emerges as a potentially green and energy-efficient approach with significant scientific value in the selective cleavage of C-C bonds. On the Pd(100) surface, it is experimentally revealed that C2 to C4 α-olefins undergo selective thermal cleavage to form surface acetylene and hydrogen. The high selectivity toward acetylene is attributed to the 4-fold hollow sites which are adept at severing the terminal double bonds in α-olefins to produce acetylene. A challenge arises, however, because acetylene tends to stay at the Pd(100) surface. By using the surface alloying methodology with alien Au, the surface Pd d-band center has been successfully shifted away from the Fermi level to release surface-generated acetylene from α-olefins as a gaseous product. Our study actually provides a technological strategy to economically produce acetylene and hydrogen from α-olefins.

Structural transitions in liquid semiconductor alloys: A molecular dynamics study with a neural network potential.

Liquid-liquid phase transitions hold a unique and profound significance within condensed matter physics. These transitions, while conceptually intriguing, often pose formidable computational challenges. However, recent advances in neural network (NN) potentials offer a promising avenue to effectively address these challenges. In this paper, we delve into the structural transitions of liquid CdTe, CdS, and their alloy systems using molecular dynamics simulations, harnessing the power of an NN potential named LaspNN. Our investigations encompass both pressure and temperature effects. Through our simulations, we uncover three primary liquid structures around melting points that emerge as pressure increases: tetrahedral, rock salt, and close-packed structures, which greatly resemble those of solid states. In the high-temperature regime, we observe the formation of Te chains and S dimers, providing a deeper understanding of the liquid's atomic arrangements. When examining CdSxTe1-x alloys, our findings indicate that a small substitution of S by Te atoms for S-rich alloys (x > 0.5) exhibits a structural transition much different from CdS, while a large substitution of Te by S atoms for Te-rich alloys (x < 0.5) barely exhibits a structural transition similar to CdTe. We construct a schematic diagram for liquid alloys that considers both temperature and pressure, providing a comprehensive overview of the alloy system's behavior. The local aggregation of Te atoms demonstrates a linear relationship with alloy composition x, whereas that of S atoms exhibits a nonlinear one, shedding light on the composition-dependent structural changes.

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.

Insights into Stereoselectivity Switch in Michael Addition-Initiated Tandem Mannich Cyclizations and Their Extension from Enamines to Vinyl Ethers.

Both cis- and trans- tetracyclic spiroindolines are the core of many important biologically active indole alkaloids, but the divergent synthesis of these important motifs is largely hampered by the limited stereoselectivity control. A facile stereoinversion protocol is reported here in Michael addition-initiated tandem Mannich cyclizations for constructing tetracyclic spiroindolines, providing an easy access to two diastereoisomeric cores of monoterpene indole alkaloids with high selectivity. The mechanistic studies including in situ NMR experiments, control experiments, and DFT calculations reveal that the reaction undergoes a unique retro-Mannich/re-Mannich rearrangement including a C-C bond cleavage that is very rare for a saturated six-membered carbocycle. Insights into the stereoinversion process have been uncovered, and the major effects were determined to be the electronic properties of N-protecting groups of the indole with the aid of Lewis acid catalysts. By understanding these insights, the stereoselectivity switching strategy is also smoothly applied from enamine substrates to vinyl ether substrates, which are enriched greatly for the divergent synthesis and stereocontrol of monoterpene indole alkaloids. The current reaction also proves to be very practical and was successfully applied to the gram-scale total synthesis of strychnine and deethylibophyllidine in short routes.

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.

Unraveling different influences of the fraction of the tetragonal phase in oxide films on the corrosion resistance of Zr alloys from the phase transition mechanism.

The mechanism of Sn and Nb influence on the fraction of tetragonal ZrO2 in oxide films on Zr alloys and their influence mechanism on corrosion resistance of Zr alloys, despite decades of research, are ambiguous due to the lack of kinetic knowledge of phase evolution of ZrO2 with doping. Using stochastic surface walking and density functional theory calculations, we investigate the influence of Nb and Sn on the stability of tetragonal (t) and monoclinic (m) ZrO2, and t-m phase transition in oxide films. We found that though Nb and Sn result in similar apparent variation trends in the t-phase fraction in oxide films, their influences on t-m phase transition differ significantly, which is the underlying origin of different influences of the t-phase fraction in oxide films on the corrosion resistance of Zr alloys with Sn and Nb alloying. These results clarify an important aspect of the relationship between the microstructure and corrosion resistance of Zr alloys.

Electrochemical hydrogen evolution on Pt-based catalysts from a theoretical perspective.

Hydrogen evolution reaction (HER) by splitting water is a key technology toward a clean energy society, where Pt-based catalysts were long known to have the highest activity under acidic electrochemical conditions but suffer from high cost and poor stability. Here, we overview the current status of Pt-catalyzed HER from a theoretical perspective, focusing on the methodology development of electrochemistry simulation, catalytic mechanism, and catalyst stability. Recent developments in theoretical methods for studying electrochemistry are introduced, elaborating on how they describe solid-liquid interface reactions under electrochemical potentials. The HER mechanism, the reaction kinetics, and the reaction sites on Pt are then summarized, which provides an atomic-level picture of Pt catalyst surface dynamics under reaction conditions. Finally, state-of-the-art experimental solutions to improve catalyst stability are also introduced, which illustrates the significance of fundamental understandings in the new catalyst design.

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).

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.

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.

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.

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.

Steering the Glycerol Electro-Reforming Selectivity via Cation-Intermediate Interactions.

Electro-reforming of renewable biomass resources is an alternative technology for sustainable pure H2 production. Herein, we discovered an unconventional cation effect on the concurrent formate and H2 production via glycerol electro-reforming. In stark contrast to the cation effect via forming double layers in cathodic reactions, residual cations at the anode were discovered to interact with the glycerol oxidation intermediates to steer its product selectivity. Through a combination of product analysis, transient kinetics, crown ether trapping experiments, in situ IRRAS and DFT calculations, the aldehyde intermediates were discovered to be stabilized by the Li+ cations to favor the non-oxidative C-C cleavage for formate production. The maximal formate efficiency could reach 81.3 % under ≈60 mA cm-2 in LiOH. This work emphasizes the significance of engineering the microenvironment at the electrode-electrolyte interface for efficient electrolytic processes.

In Situ Active Site for CO Activation in Fe-Catalyzed Fischer-Tropsch Synthesis from Machine Learning.

In situ-formed iron carbides (FeCx) are the key components responsible for Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) on Fe-based catalysts in industry. The true active site is, however, highly controversial despite more than a century of study, which is largely due to the combined complexity in both FeCx structures and mechanism of CO hydrogenation. Herein powered by machine learning simulation, millions of structure candidates for FeCx bulk and surfaces are explored under FTS conditions, which leads to resolving the active site for CO activation. This is achieved without a priori input from experiment by first constructing the thermodynamics convex hull of bulk phases, followed by identifying the low surface energy surfaces and evaluating the adsorption ability of CO and H, and finally determining the lowest energy reaction pathway of CO activation. Rich information on FeCx structures and CO hydrogenation pathways is gleaned: (i) Fe5C2, Fe7C3, and Fe2C are the three stable bulk phases under FTS in producing olefins, where Fe7C3 and Fe2C have multiple energetically nearly degenerate bulk crystal phases; (ii) only three low surface energy surfaces of these bulk phases, namely, χ-Fe5C2(510), χ-Fe5C2(111), and η-Fe2C(111), expose the Fe sites that can adsorb H atoms exothermically, where the surface Fe:C ratio is 2, 1.75, and 2, respectively; (iii) CO activation via direct dissociation can occur at the surface C vacancies (e.g., with a barrier of 1.1 eV) that are created dynamically via hydrogenation. These atomic-level understandings facilitate the building of the structure-activity correlation and designing better FT catalysts.

In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment.

PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO2 supported Pd1Ag3 catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd1Ag1 (R3̅m) and Pd1Ag3 (Pm3̅m), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets.

Recognition of Surface Oxygen Intermediates on NiFe Oxyhydroxide Oxygen-Evolving Catalysts by Homogeneous Oxidation Reactivity.

NiFe oxyhydroxide is one of the most promising oxygen evolution reaction (OER) catalysts for renewable hydrogen production, and deciphering the identity and reactivity of the oxygen intermediates on its surface is a key challenge but is critical to the catalyst design for improving the energy efficiency. Here, we screened and utilized in situ reactive probes that can selectively target specific oxygen intermediates with high rates to investigate the OER intermediates and pathway on NiFe oxyhydroxide. Most importantly, the oxygen atom transfer (OAT) probes (e.g., 4-(diphenylphosphino) benzoic acid) could efficiently inhibit the OER kinetics by scavenging the OER intermediates, exhibiting lower OER currents, larger Tafel slopes, and larger kinetic isotope effect (KIE) values, while probes with other reactivities demonstrated much smaller effects. Combining the OAT reactivity with electrochemical kinetic and operando Raman spectroscopic techniques, we identified a resting Fe═O intermediate in the Ni-O scaffold and a rate-limiting O-O chemical coupling step between a Fe═O moiety and a vicinal bridging O. DFT calculation further revealed a longer Fe═O bond formed on the surface and a large kinetic energy barrier of the O-O chemical coupling step, corroborating the experimental results. These results point to a new direction of liberating lattice O and expediting O-O coupling for optimizing NiFe-based OER electrocatalyst.

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration.

Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.The recent advances of machine-learning (ML)-based large-scale atomic simulations has shown great promise to the benefit of many branches of chemistry. Instead of solving the Schrödinger equation directly, ML-based simulations rely on a large data set of accurate potential energy surfaces (PESs) and complex numerical models to predict the total energy. These simulations feature both a high speed and a high accuracy for computing large systems. Due to the lack of a physical foundation in numerical models, ML models are often frustrated in their predictivity and robustness, which are key to applications. Focusing on these concerns, here we overview the recent advances in ML methodologies for atomic simulations on three key aspects. Namely, the generation of a representative data set, the extensity of ML models, and the continuity of data representation. While global optimization methods are the natural choice for building a representative data set, the stochastic surface walking method is shown to provide the desired PES sampling for both minima and transition regions on the PES. The current ML models generally utilize local geometrical descriptors as an input and consider the total energy as the sum of atomic energies. There are many flavors of data descriptors and ML models, but the applications for material and reaction predictions are still limited, not least because of the difficulty to train the associated vast global data sets. We show that our recently designed power-type structure descriptors together with a feed-forward neural network (NN) model are compatible with highly complex global PES data, which has led to a large family of global NN (G-NN) potentials.Two recent applications of G-NN potentials in material and reaction simulations are selected to illustrate how ML-based atomic simulations can help the discovery of new materials and reactions.

Deciphering and Suppressing Over-Oxidized Nitrogen in Nickel-Catalyzed Urea Electrolysis.

Urea electrolysis is a prospective technology for simultaneous H2 production and nitrogen suppression in the process of water being used for energy production. Its sustainability is currently founded on innocuous N2 products; however, we discovered that prevalent nickel-based catalysts could generally over-oxidize urea into NO2 - products with ≈80 % Faradaic efficiencies, posing potential secondary hazards to the environment. Trace amounts of over-oxidized NO3 - and N2 O were also detected. Using 15 N isotopes and urea analogues, we derived a nitrogen-fate network involving a NO2 - -formation pathway via OH- -assisted C-N cleavage and two N2 -formation pathways via intra- and intermolecular coupling. DFT calculations confirmed that C-N cleavage is energetically more favorable. Inspired by the mechanism, a polyaniline-coating strategy was developed to locally enrich urea for increasing N2 production by a factor of two. These findings provide complementary insights into the nitrogen fate in water-energy nexus systems.