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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

Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features

Exploring generic fractions of multi-origin asphalts and revisiting the linkage to their bulk properties via machine learning