ArticleChemical science2019
Machine learning enables long time scale molecular photodynamics simulations.
Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Modern Cyanine Dye-Based Photosensitizers for Medical Applications.Angewandte Chemie (International ed. in English) · 2026Review
- Automated Discovery of Algorithms for Molecular Electronic Structure Calculations Using Physics-Informed Program Synthesis.Journal of the American Chemical Society · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations.Journal of chemical theory and computation · 2025Article
- Perspective: Vibronic Coupling Potentials for Trajectory-Based Excited-State Dynamics.Journal of chemical theory and computation · 2025Review
- Roadmap for Molecular Benchmarks in Nonadiabatic Dynamics.The journal of physical chemistry. A · 2025Review
- 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
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model.The journal of physical chemistry letters · 2025Article
- Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal.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
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Machine Learning Approach to Vertical Energy Gap in Redox Processes.Journal of chemical theory and computation · 2024Article
- A Euclidean transformer for fast and stable machine learned force fields.Nature communications · 2024Article
- Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment.Chemical reviews · 2024Review
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
- Analytic Non-adiabatic Couplings for Selected Configuration Interaction via Approximate Degenerate Coupled Perturbed Hartree-Fock.Journal of chemical theory and computation · 2023Article
- What Controls the Quality of Photodynamical Simulations? Electronic Structure Versus Nonadiabatic Algorithm.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
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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.