Neural network potentials for exploring condensed phase chemical reactivity
Recent advances in machine learning offer powerful tools for exploring complex reaction mechanisms in condensed phases via reactive simulations. In this tutorial review, we describe the key challenges associated with simulating reactions in condensed phases, we introduce neural network potentials an...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Académie des sciences
2024-06-01
|
| Series: | Comptes Rendus. Chimie |
| Subjects: | |
| Online Access: | https://comptes-rendus.academie-sciences.fr/chimie/articles/10.5802/crchim.315/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Recent advances in machine learning offer powerful tools for exploring complex reaction mechanisms in condensed phases via reactive simulations. In this tutorial review, we describe the key challenges associated with simulating reactions in condensed phases, we introduce neural network potentials and detail how they can be trained. We emphasize the importance of active learning to construct the training set, and show how these reactive force fields can be integrated with enhanced sampling techniques, including transition path sampling. We illustrate the capabilities of these new methods with a selection of applications to chemical reaction mechanisms in solution and at interfaces. |
|---|---|
| ISSN: | 1878-1543 |