Machine Learning Interatomic Potential– tag –
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Chemistry World Reports AI Agents and MLIPs Accelerating Catalyst Discovery from Simulation to Scale-Up
Chemistry World UK Overview Chemistry World reported on the forefront of AI agents and Machine Learning Interatomic Potentials (MLIPs) accelerating the catalyst discovery process from simulation to scale-up. MLIPs replace computationally... -
ML SNAP Outperforms MEAM in Liquid (U,Zr) Thermophysical and Structural Predictions, Unveiling Viscosity Anomalies and Icosahedral Short-Range Order
PubMed International Overview In predicting the thermophysical and structural properties of liquid Uranium-Zirconium (U,Zr) mixtures, the machine learning-based Spectral Neighbor Analysis Potential (SNAP) demonstrated superior predictive... -
MLIPs Tackle Electronic Entropy Challenge: Charge State Embedding Boosts Battery Material Prediction Accuracy
arXiv International Overview Traditional Machine Learning Interatomic Potentials (MLIPs) have struggled to capture electronic entropy in mixed-valence materials, leading to prediction inaccuracies. To address this, a new approach embeds ... -
DP-EVA Framework Maximizes Pre-Trained Knowledge of Large Atomistic Models to Develop Data-Efficient MLIPs
Clean Energy | Oxford Academic International Overview A new data-efficient fine-tuning framework, DP-EVA, has been introduced, enabling the development of domain-specific Machine Learning Interatomic Potentials (MLIPs) by maximizing the ... -
arXiv Paper Presents ‘AutoPot’: Automated, Massively Parallel Workflow for Constructing Machine-Learning Potentials
arXiv USA Overview A new preprint on arXiv introduces 'AutoPot,' an automated and massively parallelized workflow for constructing Machine Learning Interatomic Potentials (MLIPs). MLIPs bring quantum accuracy to atomic modeling, enabling... -
arXiv Paper Identifies Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
arXiv USA Overview A new arXiv paper reviews the rapid advancements of machine learning (ML) in atomic modeling and the growing centrality of interatomic potentials in materials science. It discusses six open questions as key challenges ... -
SCM Releases ‘AMS2026’ Software, Accelerating Materials Chemistry Simulations with ML Potentials and GPU Optimization
SCM (Scientific Computing & Modelling) Netherlands Overview SCM announced the release of 'AMS2026' software, featuring major advancements in machine learning potentials (eSEN, MACE, UMA) that expand chemical coverage for biomolecules, ca... -
ResearchGate: NequIP GNN Predicts Amorphous Material Many-Body Interactions at 10,000x Lower Cost than DFT
ResearchGate Unknown Overview Recent research applied NequIP, an equivariant message passing graph neural network (GNN), to predict many-body interactions in model soft glasses of solvent-free polymer-grafted nanoparticles (PGNs). NequIP... -
ASM International Highlights Accelerated Computational Materials Design Integrating CALPHAD, DFT, MLIPs, and AI Agents
ASM International USA Overview An ASM International webinar showcased recent advancements in accelerating computational materials design by integrating CALPHAD, Density Functional Theory (DFT), Machine Learning Interatomic Potentials (ML...
