Pre-onlines

Advances in Graph Neural Networks for alloy design and properties predictions: a review

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

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

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

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

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

Machine learning-driven new paradigm for Co-based superalloys

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

Inverse design of high-performance Mg-Gd based magnesium alloys by machine learning method

Enhanced multi-tuple extraction for materials: integrating pointer networks and augmented attention

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

A data-driven comparative study of thermomechanical properties in rare-earth zirconate and tantalate oxides for thermal barrier coatings