Density Functional Theory– tag –
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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... -
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... -
Polymorphism and Dimensionality in Rhodium Chalcogenide Nanocrystals Enable Phase-Selective Synthesis and Structure-Dependent Property Exploration
ChemRxiv Unknown Overview This preprint explores polymorphism and dimensionality in rhodium chalcogenide nanocrystals, demonstrating how precursor choice and synthesis temperatures enable phase-selective synthesis. Analyses confirm the c... -
Computational Design and Experimental Validation Lead to Sustainable Polysaccharide Hydrogels for Oil Spill Remediation
ChemRxiv Unknown Overview This preprint investigates the computational design and experimental validation of ecofriendly polysaccharide hydrogels surface-modified with non-toxic polydimethylsiloxane (PDMS) for sustainable oil spill remed... -
Matlantis Webinar Accelerates Computational Materials Design with Integrated CALPHAD, DFT, MLIPs, and AI Agents
Matlantis Japan Overview Matlantis offers an on-demand webinar accelerating computational materials design by integrating CALPHAD, Density Functional Theory (DFT), Machine Learning Interatomic Potentials (MLIPs), and AI-assisted simulati... -
4th CEMDI-PAIMS Symposium Accelerates Materials Discovery Through Computational Materials Science, AI, Data, Experimental Insights, and International Collaboration
EurekAlert! USA Overview The 4th CEMDI-PAIMS Symposium in Montreal discussed advancements in materials discovery through the fusion of computational materials science, AI, data, experimental insights, and international collaboration. Thi...
