Inherent structural descriptors via machine learning

E Telari and A Tinti and M Settem and C Guardiani and LK Kunche and M Rees and H Hoddinott and M Dearg and B von Issendorff and G Held and TJA Slater and RE Palmer and L Maragliano and R Ferrando and A Giacomello, REPORTS ON PROGRESS IN PHYSICS, 88, 068002 (2025).

DOI: 10.1088/1361-6633/add95b

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.

Return to Publications page