Density Functional Theory– tag –
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Scalable Isotropic Message Passing ML Accelerates Electronic Structure and Atomistic Property Modeling by Efficiently Describing Non-Local Effects
The Journal of Chemical Physics | AIP Publishing USA Overview A new study demonstrates a scalable machine learning approach for isotropic message passing, leveraging continuous products of external potentials to model electronic structur... -
arXiv Paper Demonstrates Full-Data Accuracy with Fewer Labels in ML Force Field Training Through Data Selection Strategy
arXiv International Overview This paper investigates the critical role of data selection in training and fine-tuning machine-learning force fields (MLFFs), demonstrating that active learning strategies like LLPR (Least-Likely to Predict ... -
MLIP Studio Launches Free Web App ‘MLIP Studio,’ Accelerating Molecular and Materials Simulations with Over 60 Universal MLPs
MLIP Studio Blog Unknown Overview MLIP Studio has released 'MLIP Studio,' a free web application enabling atomic calculations for molecules and materials using universal machine-learning interatomic potentials (MLIPs). This application i... -
arXiv Paper Examines Limitations and Potential of MACE and CHGNet Foundation MLIPs in d4/d6/d7 Perovskite Oxide MD Simulations
arXiv International Overview This paper provides a detailed examination of the successes and limitations of foundation machine-learning interatomic potentials (MLIPs) like MACE and CHGNet in molecular dynamics (MD) simulations of d4/d6/d... -
YouTube Channel yaavikmaterials: ML Interatomic Potentials Enable Large-Scale MD with DFT Accuracy, Resolving System Size Trade-offs
yaavikmaterials (YouTube) Unknown Overview A YouTube video by yaavikmaterials explains how machine-learning interatomic potentials (MLIPs) like MACE, NequIP, and CHGNet are resolving the system size and simulation time trade-offs in mole... -
GPUMD 4.0 from Chalmers University Unleashes Machine Learning Power for Advanced Materials Simulations
Computational Materials Group @ Chalmers Sweden Overview Chalmers University of Technology's Computational Materials Group has released GPUMD 4.0, a high-performance molecular dynamics (MD) software that integrates cutting-edge machine l... -
arXiv Paper Benchmarks 6 Universal ML Interatomic Potentials, Evaluating Structural Fidelity and Performance for Lunar Regolith MD Simulations
arXiv International Overview Six universal machine-learning interatomic potentials (MLIPs)—MACE-MH, MatterSim, SevenNet-0, UPET, UMA, and NequIP-OAM-L—were comprehensively benchmarked for molecular dynamics (MD) simulations of lunar rego... -
LAMMPS Integrates Diverse Machine Learning Potentials Like MACE and CHGNet, Enhancing Atomic Simulation Versatility
LAMMPS Molecular Dynamics Simulator Unknown Overview The LAMMPS molecular dynamics simulator has significantly enhanced its interoperability with leading machine learning potential (MLP) frameworks, including MACE, CHGNet, DeePMD-kit, Ne... -
Benchmark of 23 ML Interatomic Potentials Reveals Large Models Offer Minimal Accuracy Gains at Significant Speed Cost
arXiv News International Overview A comprehensive benchmark study of 23 open-source machine-learning interatomic potentials (MLIPs) has revealed a critical trade-off between accuracy and speed. The findings indicate that large, state-of-... -
Machine Learning Designs Biomedical High-Entropy Alloys for Additive Manufacturing, Selecting Optimal Zr Alloys from 15 Million Compositions
Taylor & Francis Group - Figshare International Overview A machine learning framework has been successfully introduced and validated for designing biomedical high-entropy alloys (BioHEAs) for additive manufacturing (AM), identifying opti...
