Machine Learning Interatomic Potential– tag –
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Universal MLIPs Face Generalization Challenges, ACS Study Highlights Need for Iterative Fine-Tuning of Material-Specific Models
ACS Publications USA Overview An ACS Publications paper highlights that while Universal Machine Learning Interatomic Potentials (MLIPs) are rapidly becoming general tools for atomic simulations, their role in quantitative material modeli... -
Fine-Tuned MACE Foundation Model Develops Transferable MLP Predicting Water Adsorption in 420 Al-MOFs with DFT Accuracy
ACS Publications USA Overview This paper successfully developed a transferable machine learning potential (MLP) by fine-tuning a MACE foundation model to accurately predict water adsorption behavior in Metal-Organic Frameworks (MOFs). Tr... -
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... -
Machine Learning Interatomic Potentials (MLIPs) Accelerate Catalyst Discovery, Achieving DFT-Level Accuracy at Low Cost
nano-matter.com International Overview As of August 2026, Machine Learning Interatomic Potentials (MLIPs) have become a mainstream technology in computational catalyst research, enabling rapid screening of catalyst candidates, efficient ... -
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... -
Accelerating Deep-Ultraviolet Nonlinear Optical Material Discovery: Integrated MLIP-First Principles Framework Demonstrated in LiB2O3F
ACS Publications USA Overview An integrated framework for accelerating the discovery of deep-ultraviolet (deep-UV) nonlinear optical materials has been established, demonstrating its efficacy in the LiB2O3F system. This framework combine... -
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... -
Machine-Learned Interatomic Potential Developed for Tungsten-Boron Surface Sputtering via tabGAP Framework, Guiding MXene Defect Engineering
arXiv International Overview A machine-learned interatomic potential (MLIP) has been developed using the tabGAP framework to simulate sputtering behavior of tungsten-boron (W-B) surfaces at large scales. Trained with density functional t... -
COMPACK Program Aids CSP-MACE-Å in Temperature-Dependent Polymorph Prediction via Distance-Based Crystal Structure Similarity Identification
ResearchGate International Overview A research paper introduces 'COMPACK,' a program for identifying crystal structure similarity using distances. This program supports organic crystal structure prediction (CSP), where generative models ...
