Materials Informatics– category –
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U.S. DOE Propels AI Innovation Ecosystem, Empowering Research with Autonomous Laboratories
Department of Energy USA Overview The U.S. Department of Energy (DOE) is fostering a world-leading AI innovation ecosystem by integrating AI across high-performance computing, environmental modeling, and materials research. This includes... -
AI Workflow Unites Foundation Models and Bayesian Optimization for Cost-Efficient Materials Discovery
arXiv USA Overview A new preprint introduces a novel Bayesian materials design workflow that integrates surrogate-gated generation with foundation model embeddings. This approach, leveraging pre-trained ORB embeddings and Gaussian proces... -
AI-Powered Latent Genetic Algorithm Accelerates Crystal Structure Prediction
arXiv USA Overview A new 'Latent Genetic Algorithm (LGA),' detailed in an arXiv preprint, revolutionizes crystal structure prediction (CSP) by leveraging latent representations learned from pre-trained generalized interatomic potentials ... -
DOE Unleashes AI-Powered Autonomous Labs to Revolutionize Energy and Biotech Discovery
Department of Energy USA Overview The U.S. Department of Energy (DOE) is spearheading a transformative initiative to accelerate scientific discovery in critical sectors like energy, computing, and biotechnology through advanced AI-driven... -
MIT Consortium Tackles AI ‘Hallucinations’ to Fast-Track Battery and Semiconductor Materials Discovery
MIT Generative AI Impact Consortium USA Overview The MIT Generative AI Impact Consortium is addressing the critical issue of 'hallucinations' in large language model (LLM)-based agents when applied to materials science research. By devel... -
Tokyo Institute of Science Deciphers AI’s Material Property Predictions, Accelerating Design
東京科学大学 Japan Overview Researchers at Tokyo Institute of Science have developed an interpretable AI (XAI) methodology that reveals how AI models predict material properties, particularly light absorption spectra, from atomic structu... -
Notre Dame Leverages Data-Centric AI and Foundation Models to Accelerate Gas Separation Polymer Membrane Discovery
University of Notre Dame (Curate ND) USA Overview A Ph.D. dissertation from the University of Notre Dame introduces a novel framework leveraging data-centric machine learning and foundation models to significantly accelerate molecular di... -
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... -
Tohoku University Launches AI-Driven ‘DigCat 4.0’ Platform to Revolutionize Catalyst Discovery
東北大学 Japan Overview Tohoku University researchers have unveiled 'DigCat 4.0,' an AI-powered digital catalysis platform designed to significantly accelerate catalyst discovery and development. By integrating experimental data, theoret...
