Machine Learning Interatomic Potential– tag –
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LLMs and Autonomous Labs Revolutionize Materials Discovery, Accelerating Design with Foundational MLIPs and Generative AI
Chemistry Reviews Unknown Overview This comprehensive review highlights how AI is transforming materials science, with Large Language Models (LLMs) now extracting knowledge and generating simulation workflows. Machine Learning Interatomi... -
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... -
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... -
Machine Learning Penetrates Materials Science, Accelerating Autonomous Discovery with LLMs
Google Cloud Vertex AI Search USA Overview A comprehensive review highlights the transformative impact of machine learning, especially Graph Neural Networks, ML Interatomic Potentials, and Large Language Models (LLMs), on materials scien... -
AI-Powered Latent Genetic Algorithm Accelerates Crystal Structure Prediction
arXiv USA Overview A new 'Latent Genetic Algorithm (LGA),' detailed in an arXiv preprint, revolutionizes crystal structure prediction (CSP) by leveraging latent representations learned from pre-trained generalized interatomic potentials ... -
Accelerating Materials Discovery: Ensemble Uncertainty Quantification Enhances Neural Network Interatomic Potentials
arXiv USA Overview A recent comparative study rigorously evaluates ensemble-based uncertainty quantification (UQ) methods for Neural Network Interatomic Potentials (NNIPs), aiming to develop robust machine learning interatomic potentials... -
Chalmers Pushes Automated Machine Learning for Breakthroughs in Computational Chemistry and Materials Science
Chalmers University of Technology (AIMLeNS) スウェーデン Overview A recent AI4Science seminar at Chalmers University of Technology underscored the transformative impact of machine learning (ML) on computational chemistry and materials re... -
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 ...
