Density Functional Theory– tag –
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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... -
AI-Integrated Advanced Materials Manufacturing Accelerates Innovation in Energy Storage, Catalysis, and Environmental Applications
Frontiers (Journal of Energy Research & Reviews) International Overview AI-integrated manufacturing of advanced materials is reported to be bringing revolutionary changes to energy storage, catalysis, and environmental applications. Mach... -
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... -
Universal Foundation Model ‘NEP89’ Achieves 3-Order-of-Magnitude Computational Efficiency Boost for Atomic Simulations Across 89 Elements
(unknown, likely a preprint server like arXiv or journal) International Overview A new foundation model, 'NEP89,' based on a neuroevolution potential architecture, has been introduced, covering inorganic and organic materials across 89 e... -
Quantum Computing Revolutionizes Computational Physics, Enabling Electron-Structure-Level Precision in Materials and Chemical Simulations
Quantum + AI Insiders USA Overview Quantum computing is reported to be revolutionizing computational physics, bringing unprecedented electron-structure-level precision to molecular and material simulations in materials science and chemis... -
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... -
Foundation Models Uncover Novel High-Pressure Phase Ca6FeNi, Revolutionizing Materials Discovery Workflow
arXiv USA Overview A recent preprint on arXiv introduces a self-consistent foundation model-assisted crystal structure prediction (CSP) workflow that integrates evolutionary search with adaptive data selection and fine-tuning. This innov... -
MARVEL Project Marks 12 Years: From Quantum Mechanics to AI-Driven Materials Discovery, Reshaping Computational Science
EurekAlert! USA Overview The MARVEL project, celebrating its 12th anniversary, has fundamentally transformed materials discovery by integrating quantum mechanical simulations, advanced computing, and machine learning. Since its 2014 ince... -
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...
