Molecular Dynamics– tag –
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GRACE-OFF Achieves High-Precision Machine-Learned Interatomic Potentials for Organic Liquids via GRACE Architecture
Journal of Chemical Theory and Computation | ACS Publications USA Overview This study introduces GRACE-OFF, a machine-learned interatomic potential (MLIP) built on the Graph Atomic Cluster Expansion (GRACE) neural network architecture, d... -
LAMMPS Integrates DeePMD-kit, NequIP for Quantum-Accurate ML Potentials, Enhancing Molecular Dynamics Simulations
LAMMPS Molecular Dynamics Simulator USA Overview LAMMPS, a widely used open-source molecular dynamics simulator, now supports cross-code frameworks and interoperability tools for machine learning interatomic potentials (MLPs), including ... -
AI-Driven Hyperdynamics Method Revolutionizes Atomic Event Simulation for Material Durability and Aging Research
Nature Communications Unknown Overview A novel machine learning-based hyperdynamics method dramatically accelerates atomic motion simulations, transforming computational materials discovery. This automated approach for developing interat... -
Integrated Active Learning and Knowledge Distillation in MLMD Achieves 1/3 Data Efficiency with MACE Model, Outperforming DeePMD
ACS Publications USA Overview This study developed data-efficient and fast Machine Learning Molecular Dynamics (MLMD) interatomic potentials (MLIPs) by combining DeePMD and MACE models within an active learning and knowledge distillation... -
Novel MLIP Developed for Titanium Carbide MXenes: Applied to Ion Irradiation Simulations, Offering New Defect Engineering Guidance
The Royal Society of Chemistry UK Overview A machine-learned interatomic potential (MLIP) has been developed for titanium carbide MXenes, demonstrating successful application in ion irradiation simulations. Trained with density functiona... -
MACE and SevenNet Data Efficiency Evaluated for Material-Specific MLIP Construction: Achieving Ab Initio Accuracy with 2,000 AIMD Configurations
arXiv International Overview The amount of ab initio molecular dynamics (AIMD) data required to fine-tune universal machine-learned interatomic potentials (MLIPs) for material-specific applications has been quantified. Research indicates... -
LAMMPS Accelerates MLIP Integration: Revolutionizing MD Simulations with DeePMD-kit, MACE, CHGNet Foundation Models
LAMMPS Molecular Dynamics Simulator USA Overview The LAMMPS molecular dynamics simulator is enhancing its integration with several machine-learned interatomic potential (MLIP) frameworks, including DeePMD-kit, NequIP, Allegro, MACE, Seve... -
ACS Omega: Generative Models & MD Simulations Discover Electrolyte for High-Voltage, Low-Temp Li-ion Batteries
ACS Omega USA Overview Researchers deployed the generative machine learning model G-SchNet, trained on the QM9-GCDQE database, to accelerate electrolyte discovery for lithium-ion batteries operating at high voltages and low temperatures.... -
IIT KANPUR Elucidates Material Structure-Property Relationships with Computational Materials Science: Leveraging DFT, MD/MC, and PFM
MSE | IIT KANPUR India Overview IIT KANPUR emphasizes computational materials science as a powerful toolkit for solving material-related problems, citing Density Functional Theory (DFT) at the electronic level, Molecular Dynamics (MD) an... -
ACS Materials Au Publishes Comprehensive Tutorial on Machine Learning Tools for Electrocatalysis Simulations
ACS Materials Au USA Overview ACS Materials Au has released a tutorial on machine learning tools for electrocatalysis simulations, showcasing instruments like ML exchange-correlation functionals, Gaussian process optimizers, and ML inter...
