# Machine learning for electronically excited states of molecules

arXiv: https://arxiv.org/abs/2007.05320 · HTML: https://mlsanity.com/paper/2007.05320/

## Key facts

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

## Built on by (most cited first, 30 of 30)

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- [Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networks](https://mlsanity.com/paper/2105.08351/) (2021)
- [Electronic excited states in deep variational Monte Carlo](https://mlsanity.com/paper/2203.09472/) (2022)
- [Accurate Computation of Quantum Excited States with Neural Networks](https://mlsanity.com/paper/2308.16848/) (2023)
- [Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations](https://mlsanity.com/paper/2205.08306/) (2022)
- [Robust, randomized preconditioning for kernel ridge regression](https://mlsanity.com/paper/2304.12465/) (2023)
- [Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential](https://mlsanity.com/paper/2108.04879/) (2021)
- [NeuralNEB -- Neural Networks can find Reaction Paths Fast](https://mlsanity.com/paper/2207.09971/) (2022)
- [Towards a Foundation Model for Neural Network Wavefunctions](https://mlsanity.com/paper/2303.09949/) (2023)
- [QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules](https://mlsanity.com/paper/2406.14149/) (2024)
- [Electronic-structure properties from atom-centered predictions of the electron density](https://mlsanity.com/paper/2206.14087/) (2022)
- [Multi-Fidelity Machine Learning for Excited State Energies of Molecules](https://mlsanity.com/paper/2305.11292/) (2023)
- [Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties](https://mlsanity.com/paper/2407.17087/) (2024)
- [Lifelong Machine Learning Potentials](https://mlsanity.com/paper/2303.05911/) (2023)
- [Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning](https://mlsanity.com/paper/2311.00787/) (2023)
- [Electronic excited states from physically-constrained machine learning](https://mlsanity.com/paper/2311.00844/) (2023)
- [Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies](https://mlsanity.com/paper/2410.11392/) (2024)
- [Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo](https://mlsanity.com/paper/2503.19847/) (2025)
- [LFaB: Low fidelity as Bias for Active Learning in the chemical configuration space](https://mlsanity.com/paper/2508.15577/) (2025)
- [Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2103.12165/) (2021)
- [A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games](https://mlsanity.com/paper/2503.18224/) (2025)
- [Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning](https://mlsanity.com/paper/2606.02662/) (2026)
- [Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions](https://mlsanity.com/paper/2105.02048/) (2021)
- [Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties](https://mlsanity.com/paper/2209.08595/) (2022)
- [HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks](https://mlsanity.com/paper/2302.00374/) (2023)
- [Benchmarking Data Efficiency in Delta-ML and Multifidelity Models for Quantum Chemistry](https://mlsanity.com/paper/2410.11391/) (2024)
- [Inverse Design of Inorganic Compounds with Generative AI](https://mlsanity.com/paper/2604.11827/) (2026)
- [Agentic multi-fidelity learning of quasiparticle and excitonic properties](https://mlsanity.com/paper/2606.07836/) (2026)

## 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.