# Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics

arXiv: https://arxiv.org/abs/2002.07264 · HTML: https://mlsanity.com/paper/2002.07264/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 10 of 10)

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- [Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential](https://mlsanity.com/paper/2108.04879/) (2021)
- [Implementing graph neural networks with TensorFlow-Keras](https://mlsanity.com/paper/2103.04318/) (2021)
- [Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Intraction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach](https://mlsanity.com/paper/2410.22801/) (2024)
- [Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics](https://mlsanity.com/paper/2505.06711/) (2025)
- [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)
- [HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks](https://mlsanity.com/paper/2302.00374/) (2023)
- [A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors](https://mlsanity.com/paper/2505.23344/) (2025)

## Abstract (arXiv)

In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties for photodynamics simulations. The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings. The nonadiabatic couplings are learned in a phase-free manner as derivatives of a virtually constructed property by the deep learning model, which guarantees rotational covariance. Additionally, an approximation for nonadiabatic couplings is introduced, based on the potentials, their gradients and Hessians. As deep-learning method, we employ SchNet extended for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules and paves the way towards efficient photodynamics simulations of complex systems.