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Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials
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Transferable dispersion-aware machine learning interatomic potentials for multilayer transition metal dichalcogenide heterostructures
Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moiré superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12Å with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS2 and MoS2/WSe2 systems. We demonstrate that our NNP achieves DFT-level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moiré superlattices both with and without substrates.
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
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Phase selection in aluminum rare-earth metallic alloys by molecular dynamics simulations using machine learning interatomic potentials
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Atomic structure datasets for training machine learning interatomic potentials
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Machine-learned interatomic potentials: Recent developments and prospective applications
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Development of Machine Learned SNAP Potentials for Studying Radiation Damage in Materials.
Abstract not provided.
Generalization of SNAP to Arbitrary Machine-Learning Interatomic Potentials in LAMMPS.
Abstract not provided.
Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials.
Abstract not provided.
Development of SNASP Machine Learned Interatomic Potentials for Materials in Extreme Environments.
Abstract not provided.
Project update 2022: Machine Learned Uranium Potentials for Accelerated Nuclear Fuel Qualification [Slides]
Abstract not provided.
Scalable Solutions for Training Machine Learned Interatomic Potentials.
Abstract not provided.
Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs .
Abstract not provided.
Scalable Solutions for Training Machine Learned Interatomic Potentials.
Abstract not provided.
Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics.
Abstract not provided.