Inherent structural descriptors via machine learning
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.
Authors
- Lakshmi Kumar Kunche (ORCID: https://orcid.org/0000-0002-6914-0365)
- Henry Hoddinott
- Thomas J. A. Slater (ORCID: https://orcid.org/0000-0003-0372-1551)
- Malcolm Dearg (ORCID: https://orcid.org/0000-0002-5134-0094)
- Bernd von Issendorff (ORCID: https://orcid.org/0000-0002-4358-4494)
- Luca Maragliano (ORCID: https://orcid.org/0000-0002-5705-6967)
- Carlo Guardiani (ORCID: https://orcid.org/0000-0002-8914-9260)
- Manoj Settem (ORCID: https://orcid.org/0000-0001-7287-6827)
- Riccardo Ferrando (ORCID: https://orcid.org/0000-0003-2750-9061)
- Antonio Tinti (ORCID: https://orcid.org/0000-0002-6750-6503)
- Richard E. Palmer (ORCID: https://orcid.org/0000-0001-8728-8083)
- Alberto Giacomello (ORCID: https://orcid.org/0000-0003-2735-6982)
- Emanuele Telari (ORCID: https://orcid.org/0009-0009-3296-959X)
- Morgan Rees
- Georg Held
Institutions
- Marche Polytechnic University (IT)
- University of Freiburg (DE)
- Diamond Light Source (GB)
- Italian Institute of Technology (IT)
- Swansea University (GB)
- Istituto Nazionale di Fisica Nucleare, Sezione di Genova (IT)
- University of Wales Institute Cardiff (SG)
- École Polytechnique Fédérale de Lausanne (CH)
- Cardiff University (GB)
- University of Genoa (IT)
- Cardiff Metropolitan University (GB)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Reports on Progress in Physics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1088/1361-6633/add95b
- Citations
- 4
- Primary Topic
- Protein Structure and Dynamics
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- Engineering and Physical Sciences Research Council