# Physics-informed active learning for accelerating quantum chemical simulations

arXiv: https://arxiv.org/abs/2404.11811 · HTML: https://mlsanity.com/paper/2404.11811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 18 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation](https://mlsanity.com/paper/1810.11890/) (2018)
- [Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra](https://mlsanity.com/paper/1705.05907/) (2017)
- [MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows](https://mlsanity.com/paper/2310.20155/) (2023)
- [Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment](https://mlsanity.com/paper/1712.07240/) (2017)

## Built on by (most cited first, 1 of 1)

- [Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations](https://mlsanity.com/paper/2505.08195/) (2025)

## Abstract (arXiv)

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reactions. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. The code in MLatom and tutorials are available at https://github.com/dralgroup/mlatom.