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Exploring the Pareto front of strength-conductivity trade-off: interpretable machine learning for property prediction and composition design in high-strength high-conductivity Cu alloys

Machine learning-driven new paradigm for Co-based superalloys

Uncertainty estimation affects predictor selection and its calibration improves materials optimization

Multiscale simulations of Ge–Sb–Se–Te phase-change alloys for photonic memory applications

Advances in graph neural networks for alloy design and properties predictions: a review

The synergy of geometric tolerance factor and machine learning in discovering stable materials

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