Simulation– 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... -
Bias Identified in Universal Machine-Learned Interatomic Potentials; Iterative Fine-Tuning Improves Accuracy
Journal of Chemical Theory and Computation (ACS Publications) USA Overview This study thoroughly investigated intrinsic biases in universal machine-learned interatomic potentials (uMLIPs), such as MACE, and their impact on fine-tuning qu... -
Meta FAIR’s Universal MLIP ‘UMA’ Precisely Models Oxygen Plasma Interactions with 2D Materials, Advancing Semiconductor Manufacturing
arXiv Unknown Overview Meta FAIR's universal machine-learned interatomic potential (MLIP) model, UMA, has demonstrated highly accurate modeling of oxygen plasma interactions with tungsten disulfide (WS2), a 2D material, with performance ... -
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... -
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. ... -
Forbes Highlights Emerging Computing Ecosystem: AI, Quantum, Biological, and Chemical Convergence Accelerates Scientific Discovery
Forbes USA Overview Forbes reports on an emerging computing ecosystem where AI, quantum, biological, and chemical computing paradigms converge to accelerate scientific discovery. AI acts as a strategic intelligence layer, optimizing algo... -
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... -
LLM Automates Thermal Transport Screening in Co-Cr-Ni Medium-Entropy Alloys, Proving Concept for Autonomous Materials Discovery Workflow
ChemRxiv USA Overview This study presents a reproducible, closed-loop workflow integrating a Large Language Model (LLM) decision module with molecular dynamics simulations to automate thermal transport screening in Co-Cr-Ni medium-entrop...
