Pre-onlines

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

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

Machine learning-driven new paradigm for Co-based superalloys

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

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

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

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

Exploring materials data through collaboration: 2024 KRICT ChemDX Hackathon