All Articles

Machine learning assisted crystal structure prediction made simple

Applications of machine learning method in high-performance materials design: a review

Data-driven exploration and first-principles analysis of perovskite material

Borides as promising M₂AX phase materials with high elastic modulus using machine learning and optimization

A new framework for predicting tensile stress of natural rubber based on data augmentation and molecular dynamics simulation data

A deep neural network potential model for transition metal diborides

Lithium-ion battery health prognosis via electrochemical impedance spectroscopy using CNN-BiLSTM model

Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model

Data-driven strategy for bandgap database construction of perovskites and the potential segregation study

Theoretical design of dendrite-free zinc anode through intrinsic descriptors from symbolic regression

Searching new cocrystal structures of CL-20 and HMX via evolutionary algorithm and machine learning potential

Local environment interaction-based machine learning framework for predicting molecular adsorption energy

Clustering molecular energy landscapes by adaptive network embedding

Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm

High hydrogen evolution activities of dual-metal atoms incorporated N-doped graphenes achieved by coordination regulation

Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction

Investigation of dual atom doped single-layer MoS₂ for electrochemical reduction of carbon dioxide by first-principle calculations and machine-learning

Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe₃@N_xC_20-x (x = 0~4): a DFT exploration