ArticleProceedings of the National Academy of Sciences of the United States of America2024
Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Understanding the precipitation mechanism in pentavalent vanadium electrolytes through deep learning potential molecular dynamics.Chemical science · 2026Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Highly Oriented Nitrogen-Doped Flower-like ZnO Nanostructures for Boosting Photocatalytic and Photoelectrochemical Performance: A Combined Experimental and DFT Study.The journal of physical chemistry letters · 2025Article
- Nonadiabatic Field: A Conceptually Novel Approach for Nonadiabatic Quantum Molecular Dynamics.Journal of chemical theory and computation · 2025Review
- Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians.Nature communications · 2025Article
- Atomistic Origin of Microsecond Carrier Lifetimes at Perovskite Grain Boundaries: Machine Learning-Assisted Nonadiabatic Molecular Dynamics.Journal of the American Chemical Society · 2025Article
- Band Gap Narrowing in Lead-Halide Perovskites by Dynamic Defect Self-Doping for Enhanced Light Absorption and Energy Upconversion.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Ion Migration at Metal Halide Perovskite Grain Boundaries Elucidated with a Machine Learning Force Field.The journal of physical chemistry letters · 2024Article
- Identifying Rare Events in Quantum Molecular Dynamics of Nanomaterials with Outlier Detection Indices.The journal of physical chemistry letters · 2024Article
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Authors and funding
5 authors.
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Abstract
Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS
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