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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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.
citing papers explorer
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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.
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A critical assessment of bonding descriptors for predicting materials properties
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.
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Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction
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.
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NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
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.