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Primary Categories

Subcategories

Machine-Learning Potentials and Large-Scale Simulation

ML Potential Construction and Active Learning

ML Potential Construction and Active Learning

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ML interatomic potential, active learning, uncertainty sampling, DFT labels, DeePMD, MACE, NequIP, Allegro, energy and force errors
Universal Potential Assessment and Fine-Tuning

Universal Potential Assessment and Fine-Tuning

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universal MLIP, MACE-MP, CHGNet, DPA, zero-shot evaluation, fine-tuning, force error, property validation
Machine-Learning Potential Molecular Dynamics

Machine-Learning Potential Molecular Dynamics

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MLMD, long-timescale simulation, large systems, diffusion coefficient, interface reconstruction, reaction sampling, comparison with AIMD

Property Prediction and High-Throughput Screening

Structure–Property Models and High-Throughput Screening

Structure–Property Models and High-Throughput Screening

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graph neural network, descriptor model, formation energy, adsorption energy, band gap, high-throughput screening, cross-validation, DFT verification
Interpretable Models and Symbolic-Regression Descriptors

Interpretable Models and Symbolic-Regression Descriptors

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symbolic regression, SISSO, sparse descriptor, feature importance, structure–activity relation, interpretable machine learning
Data Mining and Machine Learning Modeling

Data Mining and Machine Learning Modeling

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multi-source data integration, data cleaning, denoising, missing-value handling, outlier detection, feature engineering, dimensionality reduction, regression / classification, cross-validation, SHAP interpretability, literature data extraction, spectral / cycling time-series data

Generative Design and Intelligent Optimization

Generative Models and Inverse Design

Generative Models and Inverse Design

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generative model, inverse design, crystal generation, molecular-skeleton design, property constraints, DFT stability check
Bayesian Optimization and Computational Experiment Design

Bayesian Optimization and Computational Experiment Design

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Bayesian optimization, Gaussian process, experiment design, composition-space search, computable objective, iterative recommendation

Multiscale Surrogates and Digital Twins

Surrogate Models for FEM and Phase-Field

Surrogate Models for FEM and Phase-Field

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surrogate model, phase-field acceleration, FEM surrogate, reduced-order model, parameter sweep, error assessment
Data Assimilation and Model Parameter Identification

Data Assimilation and Model Parameter Identification

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data assimilation, Bayesian calibration, parameter inversion, phase-field, finite element, fusion of observations, uncertainty
Battery Electrochemical–Thermal Model and State Estimation

Battery Electrochemical–Thermal Model and State Estimation

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electrochemical–thermal coupling, parameter identification, state estimation, operating-condition prediction, cycling data, limited-scope digital twin
Physics-Informed Neural Networks

Physics-Informed Neural Networks

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PINN, physics constraints, sparse data, continuum model, conservation constraints, extrapolation risk
Accelerated Electronic Structure and Hamiltonian Learning

Accelerated Electronic Structure and Hamiltonian Learning

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learned DFT Hamiltonian, DeepH, hybrid-functional acceleration, band structure, density of states, comparison with DFT