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ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic

Rolf David¹, Miguel de la Puente¹, Axel Gomez¹

¹PASTEUR, Département de Chimie, École Normale Supérieure, PSL University, Sorbonne Université, CNRS 75005 Paris France rolf.david@ens.psl.eu guillaume.stirnemann@ens.psl.eu damien.laage@ens.psl.eu.

Digital Discovery

|November 18, 2024

Summary

ArcaNN generates crucial training datasets for reactive machine-learning interatomic potentials (MLIPs). This framework accurately captures high-energy chemical reaction geometries, improving molecular simulations.

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Area of Science:

Computational Chemistry
Artificial Intelligence
Materials Science

Background:

Machine-learning interatomic potentials (MLIPs) offer accuracy and efficiency for molecular simulations, overcoming traditional limitations.
Accurate training datasets are critical for MLIPs, especially for chemical reactivity involving rare events.
Current methods often neglect the generation of datasets for high-energy reactive geometries.

Purpose of the Study:

Introduce ArcaNN, a framework for generating training datasets for reactive MLIPs.
Address the gap in dataset generation for chemical reactivity, particularly barrier-crossing events.
Enhance the accuracy and applicability of MLIPs in molecular dynamics simulations.

Main Methods:

Concurrent learning approach combined with advanced sampling techniques.
Automated iterative training, exploration, and configuration selection.
Energy and force labeling with emphasis on reproducibility and documentation.

Main Results:

ArcaNN effectively generates training datasets for reactive MLIPs.
Demonstrated success in modeling nucleophilic substitution and Diels-Alder reactions.
Achieved uniformly low errors along chemical reaction coordinates.

Conclusions:

ArcaNN provides a robust solution for creating high-quality datasets for reactive MLIPs.
The framework significantly improves the representation of high-energy geometries in molecular simulations.
ArcaNN has broad potential for reactive molecular dynamics and assessing MLIP quality.