For decades of catalysis research, the d-band center theory that correlates the d-band center and the adsorbate binding energy has successfully enabled the accelerated discovery of novel catalyst materials. Recent studies indicate that, on top of the d-band center value, the full consideration of the d-band shapes describing higher moments of the d-band as well as sp-band properties can help better capturing surface reactivity. However, the density-of-states (DOS) patterns themselves have never been used as a descriptor in combined computational-experimental studies. Here, we propose the full DOS patterns as a key descriptor in high-throughput screening protocols, and prove its effectiveness. For the hydrogen peroxide (H2O2) synthesis as our demo catalytic reaction, the present study focuses on discovering bimetallic catalysts that can replace the prototypic palladium (Pd) one. Through a series of screening processes based on DOS pattern similarities (evaluated using first-principles calculations) and synthetic feasibility, 9 candidates are finally proposed out of 4,350 bimetallic alloy, which then are expected to have a catalytic performance comparable to that of Pd. The subsequent experimental tests demonstrate that 4 bimetallic catalysts (Ni61Pt39, Au51Pd49, Pt52Pd48, Pd52Ni48) indeed exhibit the catalytic properties comparable to those of Pd. Moreover, we discovered a novel bimetallic (Ni-Pt) catalyst, which has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides a new opportunity for the catalyst discovery for the replacement or reduction in use of the platinum-group metals.
Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on a computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined…
Density functional theory based global geometry optimization has been used to demonstrate the crucial influence of the geometry of the catalytic cluster on the energy barriers for the CO oxidation reaction over Pd-based bimetallic nanoalloys. We show that dramatic geometry change between the reaction intermediates can lead to very high energy barriers and thus be prohibitive for the whole process. This introduces challenges for both the design of new catalysts, and theoretical methods employed. On the theory side, a careful choice of geometric configurations of all reaction intermediates is cr…
Structures of iron and cobalt bimetallic clusters for optimized chemical vapor deposition growth of single-walled carbon nanotubes
The University of Tokyo（东京大学）；Center for Multidimensional Carbon Materials (CMCM), Institute for Basic Science (IBS)（基础科学研究院多维碳材料中心）；Dalian University of Technology（大连理工大学）；University of Antwerp（安特卫普大学）
Bimetallic catalysts can undergo segregation or redistribution of the metals driven by oxidizing and reducing environments. Bragg coherent diffraction imaging (BCDI) was used to relate displacement fields to compositional distributions in crystalline Pt-Rh alloy nanoparticles. 3D images of internal composition showed that the radial distribution of compositions reverses partially between the surface shell and the core when gas flow changes between O2 and H2. Our observation suggests that the elemental segregation of nanoparticle catalysts should be highly active during heterogeneous catalysis …
We adapt the computational hydrogen electrode approach to explicitly account for photo-generated charges and use it to computationally screen for viable catalyst/co-catalyst combinations for photo-catalytic water splitting. The hole energy necessary to thermodynamically drive the reaction is employed as descriptor for the screening process. Using this protocol and hybrid-level density-functional theory we show that water oxidation on bare TiO2 surfaces is thermodynamically more complex than previously thought. This motivates a screening for suitable co-catalysts for this half-reaction, which w…
The discovery of metals as catalytic centers for nitrogen reduction reactions has stimulated great enthusiasm for single-atom catalysts. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the high throughout first principles calculations, the doping engineering can effectively regulate the NRR performance of b-Sb monolayer. Especially, the origin of activated N2 is revealed from the perspective of the electronic structure of the active center. Among the 24 transition metal dopants, Re@Sb and Tc@Sb showed the best NRR catalytic…
Defect-Engineered h-BN as a Platform for Single-Atom HER Catalysts: Descriptor Screening Refined by Electrochemical Stability Analysis
University of Belgrade − Faculty of Physical Chemistry（贝尔格莱德大学物理化学学院）；Luleå University of Technology（吕勒奥理工学院）；Serbian Academy of Sciences and Arts（塞尔维亚科学与艺术院）
Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machi…
Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
The conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane) facilitates its transportation and provides a way to synthesize higher value chemicals. The search for high-performance catalysts to achieve this conversion is the main scope of most corresponding studies in the field of OCM. Here, we present a general data-driven strategy for the search of novel catalytic materials, focusing particularly on materials useful for the OCM reaction. Our strategy is based on consistent experimental measurements and includes ab initio thermodynamics calculations and …
Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab-initio methods like density functional theory (DFT) are infeasible. We seek to explore large design spaces at relatively low computational cost by leveraging large, generalized, graph-based machine learning (ML) models, which are pretrained and therefore require no upfront data collection or training. We present catlas, a framework that distributes and automates the generation of adsorbate-surface configura…
Toward Controllable Catalyst Inverse Design via Large-Scale Autoregressive Pretraining
Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University（化学与生物分子工程系，新兴材料研究所，首尔大学）；Department of Chemical Engineering and Materials Science, Ewha Womans University（化学工程与材料科学系，成实女子大学）；Department of Chemical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University（化学工程系，系统健康科学与工程研究生院，成实女子大学）；Institute for Multiscale Matter and Systems (IMMS), Ewha Womans University（多尺度物质与系统研究所（IMMS），成实女子大学）；KU-KIST Graduate School of Converging Science and Technology, Korea University（KU-KIST融合科学与技术研究生院，韩国大学）；Department of Integrated Energy Engineering, Korea University（整合能源工程系，韩国大学）；Center for Hydrogen and Fuel Cells, Korea Institute of Science and Technology(KIST)（氢气与燃料电池中心，韩国科学技术院（KIST））
The metal-organic framework (MOF) MFU-4l containing Co(II) centers and Cl- ligands has recently shown promising redox activity. Aiming for further improved MOF catalysts for oxidation processes employing molecular oxygen we present a density-functional theory (DFT) based computational screening approach to identify promising metal center and ligand combinations within the MFU-4l structural family. Using the O2 binding energy as a descriptor for the redox property, we show that relative energetic trends in this descriptor can reliably be obtained at the hybrid functional DFT level and using sma…
High throughput theoretical methods are increasingly used to identify promising photocatalytic materials for hydrogen generation from water as a clean source of energy. While most promising water splitting candidates require co-catalyst loading and electrical biasing, computational costs to predict them apriori becomes large. It is therefore important to identify bare, bias-free semiconductor photocatalysts with small initial hydrogen production rates, often in the range of tens of nano-mols per hour, as these can become highly efficient with further co-catalyst loading and biasing. Here we re…
Unraveling a descriptor that is correlated with catalytic reactivity is essential for fast screening for the optimal catalysts for a given catalytic reaction. Herein, we propose bond-derived orbitalwise coordination number ($\overline{CN}^α$, $α$= $s$ or $d$) as a simple and accurate descriptor used for transition metal (TM) oxides that takes into account both geometrical and electronic structures around active sites, and avoids excessive computation burden. This descriptor has a strong scaling relation with the activity in oxygen electrochemistry, enabling us to readily screen for the optimal…
Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on f…
Single atom catalysts (SACs) present the ultimate level of catalyst utilization, which puts them in the focus of current research. For this reason, their understanding is crucial for the development of new efficient catalytic systems. Using Density Functional Theory calculations, model SACs consisted of nine metals (Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt and Au) on four different supports (pristine graphene, N- and B-doped graphene and graphene with single vacancy) were analyzed. Among them, only graphene with a single vacancy enables the formation of SACs, which are stable in terms of aggregation and…
In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials. In this work, we propose to use the d-band width of the muffin-tin orbital theory (to account for local coordination environment) plus electronegativity (to account for adsorbate renormalization) as a simple se…
Zeolites, microporous aluminosilicates, are amongst the most widely used catalysts in the petrochemical industry. Zeolite catalytic functionality is coupled to the distribution of tetrahedral alumina (AlO4-) and associated counter-cations throughout the aluminosilicate framework, yet little is definitively known about the factors that govern framework aluminium arrangement. It is generally accepted that all zeolites obey Lowensteins rule of aluminium avoidance and that Al-O-Al linkages are forbidden. Here, we describe the unprecedented screening of aluminium distribution in catalytically activ…
Great enthusiasm in single atom catalysts (SACs) for the N2 reduction reaction (NRR) has been aroused by the discovery of Metal (M)-Nx as a promising catalytic center. However,the performance of available SACs,including poor activity and selectivity,is far away from the industrial requirement because of the inappropriate adsorption behaviors of the catalytic centers. Through the first principles high throughput screening, we find that the rational construction of Fe-Fe dual atom centered site distributed on graphite carbon nitride (Fe2/gCN) compromises the ability to adsorb N2H and NH2, achiev…
Electrocatalysts of nitrogen reduction reaction (NRR) have attracted ever-growing attention due to its application for renewable energy alternative to fossil fuels. However, activation of inert N-N bond requires multiple complex charge injection which complicates the design of the catalysts. Here via combining atomic-scale screening and machine learning (ML) methods we explore the rational design of NRR single-atom catalysts (SACs) supported by molybdenum disulfide (MoS2). Our work reveals that the activity of NRR SACs is highly dependent on the number of unpaired d electrons of TM: positive s…
For electrochemical hydrogen evolution reaction (HER), developing high-performance catalysts without containing precious metals has been a major research focus in the current. Herein, we show the feasibility of HER catalytic enhancement in Ni-based materials based on topological engineering from hybrid Weyl states. Via a high-throughput computational screening from 140 000 materials, we identify a chiral compound NiSi is a hybrid Weyl semimetal (WSM) with showing bulk type-I and type-II Weyl nodes and long surface Fermi arcs near the Fermi level. Sufficient evidences verify that topological ch…
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration
Shanghai Institute of Applied Physics, Chinese Academy of Sciences（中国科学院上海应用物理研究所）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；Titan Holdings（泰坦控股）；University of Chinese Academy of Sciences（中国科学院大学）
Electrides are a unique class of electron-rich materials where excess electrons are localized in interstitial lattice sites as anions, leading to a range of unique properties and applications. While hundreds of electrides have been discovered in recent years, magnetic electrides have received limited attention, with few investigations into their fundamental physics and practical applications. In this work, 51 magnetic electrides (12 antiferromagnetic, 13 ferromagnetic, and 26 interstitial-magnetic) were identified using high-throughput computational screening methods and the latest Material Pr…
Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors whose predictive power extends over a wide range of adsorbates, multi-metallic transition metal surfaces and facets. The descriptors are expressed as non-linear functions of intrinsic properties of the clean catalyst surface, e.g. coordination numbers, d-band moments and density of states at the Fermi level. From a single density-functional theory calculation …
Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimization on the fly, and exploit its good performance and transferability fo…
Tailored Pt nanoparticle catalysts are promising candidates to accelerate the oxygen reduction reaction (ORR) in fuel cells. However, the search for active nanoparticle catalysts is hindered by laborious effort of experimental synthesis and measurements. On the other hand, DFT-based approaches are still time consuming and often not efficient. In this study, we introduce a computational model which enables rapid catalytic activity calculation of unstrained pure Pt nanoparticle electrocatalysts. The generic setup of the computational model is based on DFT results and experimental data obtained w…
Critical to the development of improved solid oxide fuel cell (SOFC) technology are novel compounds with high oxygen reduction reaction (ORR) catalytic activity and robust stability under cathode operating conditions. Approximately 2145 distinct perovskite compositions are screened for potential use as high activity, stable SOFC cathodes, and it is verified that the screening methodology qualitatively reproduces the experimental activity, stability, and conduction properties of well-studied cathode materials. The calculated oxygen p-band center is used as a first principle-based descriptor of …
Beyond the Black Box: An Interpretable Machine Learning Framework for Predicting Electronic Structure Microdescriptors and Structure-Performance Relationships in Fe-based Catalytic Systems
In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery, for example, it has alleviated an otherwise extremely complex and tedious process and has resulted in the development of several new drugs. Over the last decade, these automation techniques have begun being adapted in the chemical and materials community as well with the goal of exploring chemical space and pursuing the discovery and design of novel compounds …
