IRNet uses per-layer residual shortcuts in fully connected networks to achieve better prediction accuracy and training convergence than prior ML methods on OQMD and Materials Project datasets for material properties.
Machine learning in materials in formatics: recent applications and prospects
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PolyFusionAgent integrates a multimodal polymer foundation model with a literature-grounded AI agent for property prediction and inverse design of novel polymers.
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
CatBoost and other ensemble ML models achieve R² scores of 0.95, 0.916, and 0.903 on yield strength, ultimate tensile strength, and elongation for resorbable Mg alloys, with SHAP analysis highlighting processing conditions and Zn/Mn/Gd content as key drivers.
NIMS-OS is an open-source Python framework that orchestrates AI modules (Bayesian optimization, phase diagram construction) with robotic hardware (NAREE) to enable autonomous closed-loop materials exploration.
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IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
IRNet uses per-layer residual shortcuts in fully connected networks to achieve better prediction accuracy and training convergence than prior ML methods on OQMD and Materials Project datasets for material properties.