Zheng et al., 2025 - Google Patents
Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learningZheng et al., 2025
View HTML- Document ID
- 11622776139362346292
- Author
- Zheng S
- Zhang X
- Liu H
- Liang G
- Zhang S
- Zhang W
- Wang B
- Yang J
- Jin X
- Pan F
- Li J
- Publication year
- Publication venue
- Nature Communications
External Links
Snippet
Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions and environmental species is critical for advancing heterogeneous catalysis. Describing these phases through computational models faces the …
- 238000010801 machine learning 0 title abstract description 27
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Peng et al. | Human-and machine-centred designs of molecules and materials for sustainability and decarbonization | |
Ghanekar et al. | Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis | |
Wang et al. | Electric dipole descriptor for machine learning prediction of catalyst surface–molecular adsorbate interactions | |
Mou et al. | Bridging the complexity gap in computational heterogeneous catalysis with machine learning | |
Mistry et al. | How machine learning will revolutionize electrochemical sciences | |
Lian et al. | Stability and lifetime of diffusion-trapped oxygen in oxide-derived copper CO2 reduction electrocatalysts | |
Palkovits et al. | Using artificial intelligence to forecast water oxidation catalysts | |
Xu et al. | A deep-learning potential for crystalline and amorphous Li–Si alloys | |
Ding et al. | Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation | |
Ulissi et al. | Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO2 reduction | |
Cao et al. | The use of cluster expansions to predict the structures and properties of surfaces and nanostructured materials | |
Deshpande et al. | Quantifying uncertainty in activity volcano relationships for oxygen reduction reaction | |
Jung et al. | Machine-learning driven global optimization of surface adsorbate geometries | |
Levell et al. | Emerging atomistic modeling methods for heterogeneous electrocatalysis | |
Zheng et al. | Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning | |
Naserifar et al. | Artificial intelligence and QM/MM with a polarizable reactive force field for next-generation electrocatalysts | |
Cao et al. | Rational design of Pt3Ni surface structures for the oxygen reduction reaction | |
Rice et al. | Hydrogen coupling on platinum using artificial neural network potentials and DFT | |
Omranpour et al. | Machine learning potentials for heterogeneous catalysis | |
Mok et al. | Generative pretrained transformer for heterogeneous catalysts | |
Jung et al. | Electrochemical degradation of Pt3Co nanoparticles investigated by off-lattice kinetic Monte Carlo simulations with machine-learned potentials | |
Bawari et al. | Atomistic elucidation of sorption processes in hydrogen evolution reaction on a van Der Waals heterostructure | |
Cao | Recent advances in the application of machine-learning algorithms to predict adsorption energies | |
Lee et al. | Machine learning-based screening of highly stable and active ternary Pt alloys for oxygen reduction reaction | |
Araujo et al. | Supervised AI and deep neural networks to evaluate high-entropy alloys as reduction catalysts in aqueous environments |