[go: up one dir, main page]

Willow et al., 2024 - Google Patents

Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and …

Willow et al., 2024

View HTML
Document ID
16278353977597411674
Author
Willow S
Hajibabaei A
Ha M
Yang D
Myung C
Min S
Lee G
Kim K
Publication year
Publication venue
Chemical Physics Reviews

External Links

Snippet

To design new materials and understand their novel phenomena, it is imperative to predict the structure and properties of materials that often rely on first-principles theory. However, such methods are computationally demanding and limited to small systems. This topical …
Continue reading at pubs.aip.org (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/16Bioinformatics, 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/11Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/70Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
    • G06F19/701Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for molecular modelling, e.g. calculation and theoretical details of quantum mechanics, molecular mechanics, molecular dynamics, Monte Carlo methods, conformational analysis or the like
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Wen et al. Deep potentials for materials science
Unke et al. SE (3)-equivariant prediction of molecular wavefunctions and electronic densities
Unke et al. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Montes de Oca Zapiain et al. Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
Schleder et al. From DFT to machine learning: recent approaches to materials science–a review
Zhang et al. DPA-2: a large atomic model as a multi-task learner
Zhang et al. DeePCG: Constructing coarse-grained models via deep neural networks
Imbalzano et al. Uncertainty estimation for molecular dynamics and sampling
Xie et al. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Zhang et al. Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Mailoa et al. A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
Fox et al. Learning everywhere: Pervasive machine learning for effective high-performance computation
Watanabe et al. High-dimensional neural network atomic potentials for examining energy materials: some recent simulations
Willow et al. Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems
Eckhoff et al. Lifelong machine learning potentials
WO2022266395A1 (en) Adaptive discovery and mixed-variable optimization of next generation synthesizable microelectronic materials
US20240232576A1 (en) Methods and systems for determining physical properties via machine learning
France-Lanord et al. Data-driven path collective variables
Mbaye et al. Data-driven thermoelectric modeling: Current challenges and prospects
Sivan et al. Advances in materials informatics: a review
Honrao et al. Augmenting machine learning of energy landscapes with local structural information
Wu et al. Perfecting liquid-state theories with machine intelligence
Yuan et al. Foundation Models for Atomistic Simulation of Chemistry and Materials
Li et al. Efficient force field and energy emulation through partition of permutationally equivalent atoms
Liang et al. Probing reaction channels via reinforcement learning