ArticleThe journal of physical chemistry letters2020
Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.
Article in The journal of physical chemistry letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 203 citations in OpenAlex.
- Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials.Chemical science · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- Automated Discovery of Algorithms for Molecular Electronic Structure Calculations Using Physics-Informed Program Synthesis.Journal of the American Chemical Society · 2026Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Perspective: Vibronic Coupling Potentials for Trajectory-Based Excited-State Dynamics.Journal of chemical theory and computation · 2025Review
- Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations.Journal of chemical theory and computation · 2025Article
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
- COLUMBUS─An Efficient and General Program Package for Ground and Excited State Computations Including Spin-Orbit Couplings and Dynamics.The journal of physical chemistry. A · 2025Article
- Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model.The journal of physical chemistry letters · 2025Article
- Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians.Nature communications · 2025Article
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning.Nature communications · 2025Article
- Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning.npj computational materials · 2025Article
- Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.Nature communications · 2024Article
- COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework.Journal of chemical information and modeling · 2024Article
- Fast and accurate excited states predictions: machine learning and diabatization.Physical chemistry chemical physics : PCCP · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.