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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

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

A critical review of machine learning interatomic potentials and Hamiltonian

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

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

Machine learning-enabled optoelectronic material discovery: a comprehensive review