arXiv subjects

Mattia Perrone

Publications and source records attributed to Mattia Perrone.

4 recordsLinked to original sources

arXiv2025

A rigorous data-driven approach to the nucleation of defects in metals exploiting the link between kinetic properties and (dis)order parameters

Nucleation processes, through which a new structure progressively forms within a pre-existing homogeneous phase, are fundamental in materials science, but are also typically non-trivial to elucidate. Cases in which to nucleate are defects (or disorder) in an initially ordered structure make no exception. A prominent example is the nucleation of dislocations in metals, which critically govern their mechanical, electronic, thermal, and chemical properties. While atomic-level insights can be attained using, \textit{e.g.}, molecular dynamics simulations, systematically characterizing nucleation mechanisms and accurately quantifying kinetic rates remain challenging tasks. In this work, we demonstrate how the choice of the order parameter used to track the transition has a very strong effect on the accuracy of the kinetic rate predicted from the corresponding free-energy barrier and diffusion coefficient, a fact that has been often overlooked in the past. By exploiting this systematic error to our advantage, we demonstrate that it is possible to rigorously characterize the nucleation process using a data-driven scheme based on a variational principle, leading to optimal order parameters and a faithful mechanistic description. We apply this method to characterize, as a representative case study, the nucleation of dislocations in crystalline $fcc$ copper by analyzing replica molecular dynamics simulations at the elastic-plastic limit. By means of committor analysis and Langevin modeling, our approach allows to systematically rank candidate (dis)order parameters, identify the critical nuclei (transition states), and infer the free-energy landscapes. Given its general foundations, this method can be extended to nucleation phenomena in a broad class of materials.

cond-mat.mtrl-sci↗

arXiv2024

Unsupervised Tracking of Local and Collective Defects Dynamics in Metals Under Deformation

Metals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading from the early emerging (local) defects to the amplification of dislocations (collective plastic events) are not easy to track. Here, using tensile-stress atomistic simulations of a Copper lattice as a case study, we revisit this classical problem under a new perspective based on local dynamics rather than on purely structural arguments. We use a data-driven approach that allows tracking how local fluctuations emerge and accumulate in the atomic lattice in space and time, anticipating/determining the emergence of local or collective structural defects during deformation. Building solely on the general concepts of local fluctuations and spatiotemporal fluctuation correlations, this approach allows characterizing in a unique way the evolution through the elastic, plastic, and fracture phases, describing metals as complex systems where collective phenomena originate from local dynamical triggering events.

cond-mat.mtrl-sci↗

arXiv2024

Classification and Spatiotemporal Correlation of Dominant Fluctuations in Complex Dynamical Systems

The behavior of many complex systems, from nanostructured materials to animal colonies, is governed by local transitions that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding how they emerge and propagate throughout these systems represent often a challenge. Common strategies monitor specific parameters, tailored ad hoc to describe certain systems, over time. However, such approaches typically require prior knowledge of the underpinning physics and are poorly transferable to different systems. Here we present LEAP, a general, transferable, agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems simply starting from the trajectory of their constitutive units. Built on a bivariate combination of two abstract descriptors, LENS and {\tau}SOAP, the LEAP analysis allows (i) detecting the emergence of local fluctuations in simulation or experimentally-acquired trajectories of any type of multicomponent system, (ii) classifying fluctuations into categories, and (iii) correlating them in space and time. We demonstrate how LEAP, just building on the abstract concepts of local fluctuations and their spatiotemporal correlation, efficiently reveals precious insights on the emergence and propagation of local and collective phenomena in a variety of complex dynamical systems ranging from the atomic- to the microscopic-scale. Given its abstract character, we expect that LEAP will offer an important tool to understand and predict the behavior of systems whose physics is unknown a priori, as well as to revisit a variety of known complex physical phenomena under a new perspective.

physics.chem-ph↗

arXiv2021

Automatic Multi-Objective Optimization of Coarse-Grained Lipid Force Fields Using SwarmCG

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behaving correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. But the parameters obtained via such automatic methods often make use of specifically-designed interaction potentials, and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here we use lipids, for which we can avail of reference experimental data (area per lipid, bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights on the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs, as well as to improve existing ones.

cond-mat.soft↗