2026– date –
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AI Accelerates Novel Material Discovery: Machine Learning and Graph Neural Networks Drive Solar Energy and Catalyst Development
Future Science AI USA Overview AI, particularly machine learning models and graph neural networks (GNNs), is dramatically accelerating novel material discovery by identifying complex relationships between material composition, structure,... -
LLM-Based AI Agents Accelerate MOF/COF Discovery: Integrating ChatMOF and Experimental Validation for Novel Material Creation
OAE Publishing Inc. China Overview AI agents leveraging Large Language Models (LLMs) are dramatically accelerating the discovery of Metal-Organic Frameworks (MOFs) and Covalent-Organic Frameworks (COFs). Systems like ChatMOF are used for... -
Generative AI Reshapes Engineering Design: Autodesk, ANSYS Tools Compress Weeks of Work into Minutes
Facebook USA Overview Generative AI is revolutionizing engineering design by fundamentally amplifying engineers' creativity, efficiency, and innovation. This technology automates design generation, optimization, and smart prototyping, co... -
FabDreamer Establishes Generative AI’s “Image-to-Physical Workflow”: Enabling Physical Fabrication of AI-Generated Shapes
arXiv International Overview FabDreamer has established an "image-to-physical workflow" that transforms visual content created by generative AI into physically manufacturable artifacts through AI-assisted decomposition, editing, and stru... -
AI and Autonomous Labs Accelerate New Materials Discovery: DeepMind’s GNoME Reduces Months to Days in Development
Facebook (Argonne National Laboratory / ScienceDaily context) USA Overview Leveraging AI, particularly DeepMind's GNoME model and 'self-driving labs,' the new materials discovery process is being dramatically shortened from months to mer... -
UChicago Lab Discovers Climate-Action Materials via ML-Driven Workflow: Creates Methane-Separating MOFs ‘UCHI-1’ and ‘UCHI-2’
UChicago Pritzker School of Molecular Engineering (PME) USA Overview A research lab at the University of Chicago developed a machine-learning-driven, end-to-end workflow to streamline materials discovery from academic concepts to manufac... -
Novel MLIP Developed for Titanium Carbide MXenes: Applied to Ion Irradiation Simulations, Offering New Defect Engineering Guidance
The Royal Society of Chemistry UK Overview A machine-learned interatomic potential (MLIP) has been developed for titanium carbide MXenes, demonstrating successful application in ion irradiation simulations. Trained with density functiona... -
IDTechEx Report: AI Drives Materials Discovery to Design, Focusing on Generative Design and Synthesizability Challenges
IDTechEx UK Overview This article provides an overview of a market research report published by IDTechEx. The report highlights three key applications driving materials informatics: AI-assisted screening, formulation development, and gen... -
Mira Proposes “Fifth Paradigm” of AI in Materials Science: Integrating MatterGen, A-Lab, GNoME for Autonomous Discovery
Mira USA Overview Mira has proposed the "Fifth Paradigm" of AI in materials science, asserting that the integration of generative design (MatterGen), quantum chemistry simulations, and closed-loop laboratories (A-Lab) will accelerate aut... -
MACE and SevenNet Data Efficiency Evaluated for Material-Specific MLIP Construction: Achieving Ab Initio Accuracy with 2,000 AIMD Configurations
arXiv International Overview The amount of ab initio molecular dynamics (AIMD) data required to fine-tune universal machine-learned interatomic potentials (MLIPs) for material-specific applications has been quantified. Research indicates...
