Machine Learning– tag –
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LLM and MLIP Break Solid Electrolyte Discovery Bottlenecks, AI Closed-Loop Architecture Accelerates Development
arXiv Unknown Overview An innovative approach integrating large language models (LLMs) and machine learning potentials (MLIPs) is proposed to address bottlenecks in solid electrolyte discovery. A closed-loop architecture, combining AI-dr... -
DTU Unveils ‘Self-Driving Lab’: AI and Robotics Slash Materials Discovery from Decades to Days
Mirage News デンマーク Overview The Technical University of Denmark (DTU) has unveiled an innovative 'self-driving lab' where AI and robotic arms autonomously conduct chemical experiments, promising to compress new materials development ... -
Machine Learning Potentials Accelerate Quantum Chemistry by Up to 1 Million-Fold, Revolutionizing Materials Science
ACS Central Science USA Overview Rapid advancements in machine learning interatomic potentials (MLIPs) are poised to accelerate quantum chemistry calculations by up to a million times, fundamentally transforming chemical and materials sc... -
DeepMind’s GNoME and Microsoft’s MatterGen Drastically Accelerate AI-Driven Materials Discovery, Rapidly Screening Millions of Inorganic Crystals
AI CERTs News USA Overview Advanced AI pipelines like DeepMind's GNoME and Microsoft's MatterGen are leveraging graphene neural networks and machine learning potentials to screen millions of inorganic crystals at unprecedented speeds. Th... -
University of Washington Develops Self-Improving Design Loop for New Materials via AI-Quantum Computing Fusion
richardmitnick (blog) USA Overview University of Washington research has developed a self-improving design loop for new materials through the fusion of AI and quantum computing. AI simulates complex quantum behaviors in stacked atomic sh... -
Oxford Academic: Machine Learning and LLM Synergy Uncovers High-Entropy Alloy Electrocatalytic Activity, Enabling High-Throughput Discovery
National Science Review (Oxford Academic) China Overview Research published in Oxford Academic combined machine learning (including GNNs) with an LLM-driven collaborative framework to unveil correlations between high-entropy alloy (HEA) ... -
OAE Publishing Reveals Interpretable Machine Learning Deciphers Strength-Ductility Trade-off in (CuNiMn)-X Alloys, Streamlining High-Performance Copper Alloy Design
OAE Publishing Inc. China Overview OAE Publishing Inc. has presented an integrated strategy utilizing interpretable machine learning to decipher the strength-ductility trade-off in (CuNiMn)-X alloys, enabling efficient design of high-per... -
arXiv Paper Presents ML Model for High-Precision Prediction of Metallic Glass Critical Cooling Rates Using Elemental and Molecular Simulation Features
arXiv USA Overview This study presents a machine learning model for predicting critical cooling rates of metallic glasses using computationally derived properties, specifically elemental and molecular dynamics simulation-based features. ... -
MIT Develops Information Theory-Based MLP to Significantly Enhance Metal Alloy Behavior Modeling Accuracy
MIT News USA Overview MIT researchers have developed a novel framework for modeling metallic behavior using machine learning potentials (MLPs) trained on datasets that efficiently capture diverse atomic environments in chemically disorde... -
MLIP Enhanced Sampling Simulations Uncover Dynamic Conformations and Catalytic Implications of Au9 Nanocluster Confined in UiO-66-NH2 MOF
ChemRxiv USA Overview This research meticulously investigated the structural and dynamic behavior of an Au9 nanocluster confined within a UiO-66-NH2 MOF using machine learning interatomic potential (MLIP)-driven enhanced sampling simulat...
