Deep Learning– tag –
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AI to Accelerate Energy Materials Discovery: NUS Announces 2026 Workshop
National University of Singapore (NUS) Singapore Overview The National University of Singapore (NUS) will host an "AI for Energy Materials" workshop on July 10, 2026, co-located with the Solid State Ionics Conference 2026. Bringing toget... -
Nested Graph Neural Network Revolutionizes Property Prediction for Complex Solid Solutions
Edinburgh Research Explorer UK Overview Researchers at the University of Edinburgh have developed the Solid Solution Nested Graph Neural Network (SSNGNN), a novel AI framework that significantly boosts the predictive accuracy for chemica... -
Accelerating Materials Discovery: Ensemble Uncertainty Quantification Enhances Neural Network Interatomic Potentials
arXiv USA Overview A recent comparative study rigorously evaluates ensemble-based uncertainty quantification (UQ) methods for Neural Network Interatomic Potentials (NNIPs), aiming to develop robust machine learning interatomic potentials... -
Institute of Science Tokyo Develops ML Framework to Infer Semiconductor Material Parameters with High Accuracy in Under 1 Millisecond from Transistor Measurements
Institute of Science Tokyo / Advanced Intelligent Systems Japan Overview Researchers at the Institute of Science Tokyo have developed a groundbreaking machine learning framework for solving inverse problems in semiconductor materials. Th... -
Institute of Science Tokyo Dramatically Enhances Material Prediction Interpretability with ALIGNN and Clustering AI, Precisely Forecasting Optical Absorption Spectra
Lab Manager Japan Overview Researchers at the Institute of Science Tokyo have developed a novel method combining a graph neural network (ALIGNN) with hierarchical clustering to significantly improve the interpretability of AI-driven mate... -
DeepMind’s GNoME and Microsoft’s MatterGen Drastically Accelerate AI-Driven Materials Discovery, Rapidly Screening Millions of Inorganic Crystals
AI CERTs News USA Overview Advanced AI pipelines like DeepMind's GNoME and Microsoft's MatterGen are leveraging graphene neural networks and machine learning potentials to screen millions of inorganic crystals at unprecedented speeds. Th... -
MDPI Buildings Features Mechanically Constrained GNN for Enhanced Linear Static Analysis of Planar Frame Structures
MDPI Buildings Switzerland Overview This study developed a mechanically constrained Graph Neural Network (GNN) method for 2D linear elastic static analysis of planar truss and building frame structures. The method represents structural s... -
arXiv Introduces MMGNN: Multi-level, Multi-color Graph Neural Networks Decompose Molecular Graphs for Enhanced Property Prediction
arXiv (via ResearchGate) USA Overview A new Multi-level, Multi-color Graph Neural Network (MMGNN) has been introduced on arXiv, a hierarchical framework that decomposes molecular graphs into overlapping atom-type-pair-specific subgraphs.... -
Hugging Face Spotlights CrystalCLR and CHGNet for Enhanced Materials Property Prediction via Machine Learning
Hugging Face USA Overview Hugging Face highlights significant advancements in materials property prediction with the CrystalCLR framework and CHGNet machine learning interatomic potential. CrystalCLR improves material representations thr... -
Tokyo Institute of Science Unveils Explainable AI Leveraging ALIGNN and Hierarchical Clustering for High-Accuracy Optical Spectra Prediction in Materials
HyperAI (via Google News) Japan Overview Researchers at the Tokyo Institute of Science have significantly enhanced the interpretability of AI-driven materials predictions by combining the ALIGNN graph neural network with hierarchical clu...
