August 2026– date –
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Tian Xie and Ziheng Lu Unveil MatterGen, a Generative AI Framework Accelerating Materials Discovery
Facebook (Tian Xie and Ziheng Lu discussing MatterGen) USA Overview Researchers Tian Xie and Ziheng Lu have announced MatterGen, a generative AI framework poised to revolutionize materials discovery. This innovative approach leverages AI... -
GitHub’s AI4Science Repository Curates Essential AI Tools, Libraries, and Datasets to Accelerate Scientific Discovery, Including Materials Science
GitHub - ai4s-research/awesome-ai-for-science USA Overview The 'ai4s-research/awesome-ai-for-science' GitHub repository now provides a curated list of AI tools, libraries, papers, datasets, and frameworks designed to accelerate scientifi... -
Meta AI Releases OMat24, a Massive Inorganic Materials Dataset with Over 110 Million DFT Calculations, Alongside High-Performance EquiformerV2 GNN Model
Meta Fundamental AI Research (FAIR) USA Overview Meta Fundamental AI Research (FAIR) has unveiled Open Materials 2024 (OMat24), a monumental inorganic materials dataset comprising over 110 million DFT calculations, positioning it as one ... -
Graph Neural Network Accelerates Novel Material Discovery with Reduced Computational Cost for Catalysts and Batteries
PRX Intelligence USA Overview A new Graph Neural Network (GNN) model published in PRX Intelligence significantly accelerates novel material prediction and discovery while drastically lowering computational costs. The model leverages proj... -
AI-Assisted Multiobjective High-Throughput Screening of GPE Accelerates Safer Lithium Battery Development
ACS Publications USA Overview An ACS Publications paper reports a method accelerating the development of safer lithium batteries through AI-assisted multiobjective high-throughput screening and prioritization of gel polymer electrolytes ... -
Washington University Develops AI Synthesis Model Learning Recipes from Chemical Literature to Accelerate New Material Discovery
Washington University in St. Louis USA Overview Researchers at Washington University have significantly accelerated the discovery rate of new materials and chemical compounds by enabling AI systems to model chemical synthesis. This innov... -
AI-Driven Material Discovery Workflows Accelerate Nanotechnology R&D, CuspAI Demonstrates Autonomous Synthesis Pipeline
nano-matter.com International Overview AI-driven material discovery workflows are dramatically accelerating nanotechnology R&D, replacing traditional trial-and-error processes with predictive intelligence and significantly shortening... -
Princeton University and Collaborators Awarded $19.5 Million by NSF to Establish Open-Access Autonomous Chemistry Laboratory
UCLA Chemistry USA Overview Professor Abigail Doyle of Princeton University and collaborators have secured a $19.5 million National Science Foundation (NSF) award to establish an open-access, AI-enabled autonomous chemistry laboratory. T... -
U.S. Department of Energy Leverages AI and Supercomputing for Critical Mineral Recovery from Lithium-Ion Battery Recycling
GovCIO USA Overview The U.S. Department of Energy (DOE) is advancing a project at SLAC National Accelerator Laboratory, led by Stanford University, utilizing an AI agent team to efficiently recover critical minerals like nickel and cobal... -
Autonomous Materials Discovery Labs Slash Development Time from Decades to Weeks through Closed-Loop ML and Robotic Experimentation
Scifiniti International Overview The paper 'AI-Guided Self-Driving Laboratories for Advanced Materials Discovery' proposes that autonomous materials discovery platforms, integrating closed-loop machine learning and robotic experimentatio...
