ReviewNature reviews. Chemistry2021
Molecular excited states through a machine learning lens.
Review in Nature reviews. Chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
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Who cites it
59 citing papers in PubMed.
- A versatile method for accurately predicting electronic absorption spectra of tetrapyrrole macrocycles.Scientific reports · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules.Scientific data · 2026Article
- Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules.Journal of chemical theory and computation · 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
- Turning Stress Into Signal: Mechanochromic Materials in Commodity and Technologically Relevant Polymers.Chemistry, an Asian journal · 2026Review
- 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
- Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations.Journal of chemical theory and computation · 2025Article
- 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
- Modified chlorophyll pigment at ChlChemical science · 2025Article
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- Parametrization of Linear Vibronic Coupling Models for Degenerate Electronic States.The journal of physical chemistry. A · 2025Article
- Predicting Molecular Energies of Small Organic Molecules With Multi-Fidelity Methods.Journal of computational chemistry · 2025Article
- Advancing nonadiabatic molecular dynamics simulations in solids with E(3) equivariant deep neural hamiltonians.Nature communications · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
Identifiers
37118026What OpenQuestion holds
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.