Telari, Emanuele, Tinti, Antonio, Settem, Manoj, Guardiani, Carlo, Kunche, Lakshmi Kumar, Rees, Morgan, Hoddinott, Henry, Dearg, Malcolm, von Issendorff, Bernd, Held, Georg, Slater, Thomas  ORCID: https://orcid.org/0000-0003-0372-1551, Palmer, Richard E, Maragliano, Luca, Ferrando, Riccardo and Giacomello, Alberto
2025.
Inherent structural descriptors via machine learning.
Reports on Progress in Physics
88
, 068002.
10.1088/1361-6633/add95b
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Abstract
Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning (ML) approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. In addition, we illustrate the generality of this ML strategy by deploying it to understand conformational rearrangements of the bradykinin peptide, indicating its applicability to a vast range of systems, including liquids, glasses, and proteins.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Research Institutes & Centres > Cardiff Catalysis Institute (CCI) Schools > Chemistry |
| Publisher: | IOP Publishing |
| ISSN: | 0034-4885 |
| Funders: | EPSRC |
| Date of First Compliant Deposit: | 4 June 2025 |
| Date of Acceptance: | 15 May 2025 |
| Last Modified: | 09 Jun 2025 15:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/178734 |
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