July 2026– date –
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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... -
Google DeepMind and Meta Spearhead Curated AI Resources for Scientific Breakthroughs
GitHub USA Overview A new 'awesome-ai-for-science' GitHub repository, a meticulously curated list of AI tools and datasets, has been released to accelerate scientific discovery across physics, chemistry, biology, and materials science. S... -
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... -
Chalmers Pushes Automated Machine Learning for Breakthroughs in Computational Chemistry and Materials Science
Chalmers University of Technology (AIMLeNS) スウェーデン Overview A recent AI4Science seminar at Chalmers University of Technology underscored the transformative impact of machine learning (ML) on computational chemistry and materials re... -
Accelerating Innovation: Lawrence Berkeley’s A-Lab Leverages AI for Self-Driving Materials Discovery
TheSequence USA Overview Self-driving labs are fundamentally transforming materials science by autonomously executing experiments and learning from both successes and failures. Lawrence Berkeley National Laboratory's A-Lab exemplifies th... -
AI-Driven Design Transforms Metal-Organic Materials to Dynamic Networks, Powering Self-Driving Labs for Accelerated Discovery
MDPI International Overview This paper proposes extending AI-driven design of metal-organic materials (MOMs) beyond traditional crystalline MOFs to dynamic coordination networks, such as metal-polyphenol networks (MPNs). It integrates pr... -
AI and Machine Learning Drive Breakthroughs in Sustainable Catalyst Design, Expanding Applications from CO2 Conversion to Polymer Recycling
MDPI Switzerland Overview The integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming catalyst design and sustainable chemical process development. These technologies accelerate new catalyst di...
