mlsanityData to 14 Oct 2026

Machine learning for electronically excited states of molecules

arXiv 2007.05320 ↗First posted 10 Jul 2020physics.chem-ph stat.MLcited by 30

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Abstract (arXiv)

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 how machine learning is employed not only 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, approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jul 2020.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 8; 2024: 4; 2025: 4; 2026: 3.

30 papers.

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  1. 2022SELFIES and the future of molecular string representationscited by 76
  2. 2022Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentialscited by 37
  3. 2025A practical guide to machine learning interatomic potentials -- Status and futurecited by 26
  4. 2021Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networkscited by 24
  5. 2022Electronic excited states in deep variational Monte Carlocited by 21
  6. 2023Accurate Computation of Quantum Excited States with Neural Networkscited by 17
  7. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  8. 2023Robust, randomized preconditioning for kernel ridge regressioncited by 9
  9. 2021Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potentialcited by 7
  10. 2022NeuralNEB -- Neural Networks can find Reaction Paths Fastcited by 7
  11. 2023Towards a Foundation Model for Neural Network Wavefunctionscited by 7
  12. 2024QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Moleculescited by 7
18 more
  1. 2022Electronic-structure properties from atom-centered predictions of the electron densitycited by 4
  2. 2023Multi-Fidelity Machine Learning for Excited State Energies of Moleculescited by 4
  3. 2024Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Propertiescited by 4
  4. 2023Lifelong Machine Learning Potentialscited by 3
  5. 2023Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learningcited by 2
  6. 2023Electronic excited states from physically-constrained machine learningcited by 2
  7. 2024Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energiescited by 2
  8. 2025Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlocited by 2
  9. 2025LFaB: Low fidelity as Bias for Active Learning in the chemical configuration spacecited by 2
  10. 2021Automated and Autonomous Experiment in Electron and Scanning Probe Microscopycited by 1
  11. 2025A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Gamescited by 1
  12. 2026Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learningcited by 1
  13. 2021Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactionscited by 0
  14. 2022Low-cost machine learning approach to the prediction of transition metal phosphor excited state propertiescited by 0
  15. 2023HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networkscited by 0
  16. 2024Benchmarking Data Efficiency in Delta-ML and Multifidelity Models for Quantum Chemistrycited by 0
  17. 2026Inverse Design of Inorganic Compounds with Generative AIcited by 0
  18. 2026Agentic multi-fidelity learning of quasiparticle and excitonic propertiescited by 0