ArticleChemical reviews2021
Machine Learning for Electronically Excited States of Molecules.
Article in Chemical reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Modern Cyanine Dye-Based Photosensitizers for Medical Applications.Angewandte Chemie (International ed. in English) · 2026Review
- Accurate energies for ππ* excited statesChemical science · 2026Article
- The Newton-X platform for mixed quantum-classical dynamics.Physical chemistry chemical physics : PCCP · 2026Article
- Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- OMNI-P2x universal neural network potential for excited-state simulations.Nature communications · 2026Article
- Automated Discovery of Algorithms for Molecular Electronic Structure Calculations Using Physics-Informed Program Synthesis.Journal of the American Chemical Society · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules.Scientific data · 2026Article
- Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.Journal of chemical theory and computation · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Cartesian equivariant representations for learning and understanding molecular orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction.Journal of chemical theory and computation · 2025Article
- tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes.Journal of chemical information and modeling · 2025Article
- Generative design of functional organic molecules for terahertz radiation detection.Digital discovery · 2025Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Weighted active space protocol for multireference machine-learned potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Perspective: Vibronic Coupling Potentials for Trajectory-Based Excited-State Dynamics.Journal of chemical theory and computation · 2025Review
42 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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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.