Pre-onlines
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
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
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
Stacked machine learning for accurate and interpretable prediction of MXenes' work function
Advances in Graph Neural Networks for alloy design and properties predictions: a review
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
Advancing carbon dots research with machine learning: a comprehensive review
Accelerating materials discovery via AI-Agent integration of large language models and simulation tools
Machine learning-accelerated transition state prediction for strain-engineered high-entropy alloy catalysts
Uncertainty estimation affects predictor selection and its calibration improves materials optimization
Multi-objective optimization of fiber laser welding parameters for 316L stainless steel
