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Ultralow thermal conductivity via weak interactions in PbSe/PbTe monolayer heterostructure for thermoelectric design

Deep learning-enhanced cellular automaton framework for modeling static recrystallization behavior

Exploring materials data through collaboration: 2024 KRICT ChemDX Hackathon

RF-NSGA-II framework for inverse design of high-performance Mg-Gd-based magnesium alloys

Stacked machine learning for accurate and interpretable prediction of MXenes’ work function

MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science

Unlocking the future of materials science: key insights from the DCTMD workshop

Atomistic underpinnings for dislocation emission behaviors at the crack tips in FCC metals in light of thermo-kinetic synergy

FIND: a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

Machine learning-driven design and optimization of electronic packaging: applications and future developments

Heterogeneous catalyst design by generative models

Data-driven OLED candidate design: a generative model from independent-property domains to the comprehensive performance enhancement

Application of self-supervised learning in steel surface defect detection

A critical review of machine learning interatomic potentials and Hamiltonian

Accelerating phase-field simulation of coupled microstructural evolution using autoencoder-based recurrent neural networks

Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review

DSMR: an AI framework for exploring combinations of data and algorithm to overcome efficiency-accuracy trade-off

Farthest point sampling in property designated chemical feature space as an effective strategy for enhancing the machine learning model performance for small scale chemical dataset