Materials Informatics– category –
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Firefly-Geni Autonomous Framework Leverages LLMs for Generative Discovery of Thermally Activated Delayed Fluorescence (TADF) Molecular Candidates
ChemRxiv International Overview Firefly-Geni has been announced as an automated, interpretable active evolution framework integrating LLM-assisted data extraction, multitask property prediction, conditional molecule generation, and theor... -
Challenge of Atomic Partial Charges in MLIPs Impedes High-Precision Simulation of Non-Covalent Interactions
ACS Publications (Journal of Chemical Theory and Computation) International Overview While Machine Learning Interatomic Potentials (MLIPs) hold great promise for achieving DFT-level accuracy at low computational cost in molecular dynamic... -
Tokyo University of Science Discovers New Class of Ferromagnetic Quasicrystals with Machine Learning, AI Accelerates Magnetic Material Development
Digital Journal (Tokyo University of Science) Japan Overview A research team at Tokyo University of Science has successfully developed a new class of ferromagnetic quasicrystals by applying machine learning. This AI-guided approach enabl... -
AI-Integrated Advanced Materials Manufacturing Accelerates Innovation in Energy Storage, Catalysis, and Environmental Applications
Frontiers (Journal of Energy Research & Reviews) International Overview AI-integrated manufacturing of advanced materials is reported to be bringing revolutionary changes to energy storage, catalysis, and environmental applications. Mach... -
MLIP Studio Launches on arXiv, Integrating Over 60 MLIPs to Dramatically Reduce Computational Costs for Atomistic Simulations
arXiv International Overview MLIP Studio, an open-source platform, has been announced on arXiv, integrating over 60 universal Machine Learning Interatomic Potentials (MLIPs) to facilitate atomistic simulations and benchmarking. This plat... -
Royal Society of Chemistry Integrates MLIPs and Classical Force Fields for MOF Adsorption Screening, Achieving DFT Accuracy at Low Cost
The Royal Society of Chemistry (Chemical Science) International Overview An efficient hybrid screening workflow integrating classical force fields with universal machine learning interatomic potentials (u-MLIPs) has been introduced for m... -
Universal Foundation Model ‘NEP89’ Achieves 3-Order-of-Magnitude Computational Efficiency Boost for Atomic Simulations Across 89 Elements
(unknown, likely a preprint server like arXiv or journal) International Overview A new foundation model, 'NEP89,' based on a neuroevolution potential architecture, has been introduced, covering inorganic and organic materials across 89 e... -
Osaka University Develops AI-Designed Self-Healing Super-Adhesive Hydrogel with Enhanced Underwater Adhesion and Accelerated Material Discovery
The Times of India (Osaka University) Japan Overview Researchers at Osaka University have successfully developed an AI-designed super-adhesive hydrogel that exhibits strong underwater adhesion and self-healing capabilities. This AI-drive... -
Quantum Computing Revolutionizes Computational Physics, Enabling Electron-Structure-Level Precision in Materials and Chemical Simulations
Quantum + AI Insiders USA Overview Quantum computing is reported to be revolutionizing computational physics, bringing unprecedented electron-structure-level precision to molecular and material simulations in materials science and chemis... -
AI-Powered BATMAT Platform Accelerates Battery Material Discovery 100-Fold, Eyes Commercialization in 18 Months
myScience (University of Luxembourg) Luxembourg Overview Professor Alexandre Tkatchenko's team at the University of Luxembourg has introduced 'BATMAT,' a groundbreaking platform integrating AI with physics-based simulations. This closed-...
