Density Functional Theory– tag –
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South China University of Technology and Xi’an Jiaotong University Introduce ‘HULU’ to Accelerate Clean Energy Material Discovery
EurekAlert! China Overview Researchers from South China University of Technology and Xi'an Jiaotong University have developed 'HULU,' a flexible Python framework integrating advanced Machine Learning Potentials (MLPs) with Monte Carlo ad... -
AI-Powered Simulations Unlock Next-Gen Battery Design: How MLIPs Bridge the Gap Between DFT and Classical MD
ResearchGate Overview A recent review highlights how Machine Learning Interatomic Potentials (MLIPs) are effectively addressing persistent challenges in computational materials science for battery development. By bridging the gap between... -
Computational Powerhouse: AI and DFT Drive Next-Gen Sodium-Ion Battery Innovation
MDPI Overview A new MDPI review highlights the critical role of theoretical calculations, including Density Functional Theory (DFT) and Molecular Dynamics (MD), alongside Machine Learning (ML), in accelerating the discovery and optimizat... -
Atomicrex: Open-Source Platform Unlocks Large-Scale Atomic Interaction Models for Faster Materials Discovery
atomicrex Overview A new open-source code, 'atomicrex,' is poised to accelerate computational materials science by dramatically simplifying the construction of interatomic potentials for simulations involving thousands of atoms or more. ... -
Machine Learning Interatomic Potentials: Foundation Models Reshape Materials Discovery
Review of Peer-Reviewed Articles Overview A new comprehensive review critically examines Machine Learning Interatomic Potentials (MLIPs), a transformative technology bridging the accuracy of quantum mechanics with the efficiency of class... -
OMol25-Trained MLIPs Predict Na-Ion Battery Electrolyte Solvation Structures with DFT-Level Accuracy, Outperforming Inorganic-Only Models
ACS Publications (Journal of Physical Chemistry Letters) Overview This study demonstrates that machine learning interatomic potentials (MLIPs) trained with the Open Molecules 2025 (OMol25) dataset accurately predict and experimentally ve... -
Unlocking Precision: Iterative Fine-Tuning Overcomes Bias in Universal MLIPs for Enhanced Material Simulations
Unknown Source Unknown Overview Universal Machine Learning Interatomic Potentials (uMLIPs) promise broad applicability across the periodic table, yet accurate out-of-domain predictions demand specialized fine-tuning. This study reveals t... -
AI’s Three Pillars: Accelerating Materials Discovery with Predictive Models, Generative Design, and Machine Learning Interatomic Potentials
Source Unknown USA Overview Artificial intelligence is fundamentally transforming materials science through three key advancements: highly accurate property prediction, autonomous generative design, and revolutionary machine learning int... -
MIT Researchers Uncover Critical Role of Coverage-Dependent Lateral Interactions in High-Entropy Alloy Electrocatalytic Activity for Oxygen Reduction Reaction via MLIPs
PubMed USA Overview MIT researchers developed a framework utilizing Machine Learning Interatomic Potentials (MLIPs) to model the oxygen reduction reaction (ORR) within the compositional space of Ag-Ir-Ru-Pd-Pt-Cu-Rh-Re high-entropy alloy... -
Active Learning for MACE-MP-0 Foundation MLFFs Achieves Full Data Accuracy with Significantly Fewer Labeled Training Examples
arXiv International Overview This study demonstrates an innovative active learning strategy for fine-tuning machine-learning force fields (MLFFs), specifically focusing on foundation models like MACE-MP-0, to achieve full-data accuracy w...
