Dietrich, Florian M;
Salvalaglio, Matteo;
(2025)
On the reproducibility of free energy surfaces in machine-learned collective variable spaces.
The Journal of Chemical Physics
, 163
(14)
, Article 141102. 10.1063/5.0287912.
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Abstract
As Machine-Learned Collective Variables (MLCVs) are becoming increasingly relevant in the molecular simulation literature, we discuss the necessary conditions to enable reproducibility in calculating and representing free energy surfaces. We note that the variability of the training process and the roughness of the hyperparameter space impose inherent limits on the reproducibility of results even when the mathematical structure of the model defining a collective variable is consistent. To this end, we propose the adoption of a geometric (gauge invariant) free energy representation to obtain consistent free energy differences across training instances and architectures. Furthermore, we introduce a normalization factor to model gradients for biased enhanced sampling. This factor effectively unifies free energy definitions and addresses practical issues preventing the widespread use and deployment of MLCVs.
| Type: | Article |
|---|---|
| Title: | On the reproducibility of free energy surfaces in machine-learned collective variable spaces |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1063/5.0287912 |
| Publisher version: | https://doi.org/10.1063/5.0287912 |
| Language: | English |
| Additional information: | © 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215740 |
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