arXiv subjects

Fabio Pietrucci

Publications and source records attributed to Fabio Pietrucci.

17 recordsLinked to original sources

arXiv2026

Free energies and optimal reaction coordinates via entropy production

We show through theory and numerical experiments that a straightforward calculation of entropy production on short molecular dynamics trajectories allows estimating free-energy barriers and identifying optimal reaction coordinates of activated processes. To this aim, we perform an analysis based on stochastic energetics on a set of trajectories relaxing towards equilibrium from a same initial configuration, projected on different putative coordinates. After demonstrating the approach on simple benchmarks, we show that it is possible to estimate the free-energy barrier of a complex high-dimensional system (carbon nanoparticles in water) and to rank the quality of order parameters, in agreement with the committor probability. The results shed light on the entanglement between the second law, free-energy landscapes, reaction coordinates and kinetic rates.

cond-mat.stat-mech↗

arXiv2025

Correcting systematic errors in the likelihood optimization of underdamped Langevin models of molecular dynamics trajectories

Since Kramers' pioneering work in 1940, significant efforts have been devoted to studying Langevin equations applied to physical and chemical reactions projected onto few collective variables, with particular focus on the inference of their parameters. While the inference for overdamped Langevin equations is well-established and widely applied, a notable gap remains in the literature for underdamped Langevin equation. This gap arises from the challenge of accessing velocities solely through finite differences of positions, resulting in spurious correlations. In this letter, we propose an analytical correction for these correlations, specifically designed for a likelihood-maximization algorithm that exploits short, non-ergodic trajectories that can be obtained at reasonable numerical cost. The accuracy and robustness of our approach are tested on a benchmark case and a realistic system. This work paves the way for applying generalized Langevin equation inference to activated phenomena, such as chemical reactions, in several scientific domains.

cond-mat.stat-mech↗

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

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

physics.ed-ph↗

arXiv2024

Biaxial strain effects in 2D diamond formation from graphene stacks

Discovering innovative methods to understand phase transitions, modify phase diagrams, and uncover novel synthesis routes poses significant and far-reaching challenges. In this study, we demonstrate the formation of nanodiamond-like sp3 carbon from few-layer graphene (FLG) stacks at room temperature and relatively low transition pressure (~7.0 GPa) due to chemical interaction with water and physical biaxial strain induced by substrate compression. By employing resonance Raman and optical absorption spectroscopies at high-pressure on FLG systems, utilizing van der Waals heterostructures (hBN/FLG) on different substrates (SiO2/Si and diamond), we originally unveiled the key role of biaxial strain. Ab initio molecular dynamics simulations corroborates the pivotal role of both water and biaxial strain in locally stabilizing sp3 carbon structures at the graphene-ice interface. This breakthrough directly enhances nanodiamond technology but also establishes biaxial strain engineering as a promising tool to explore novel phases of 2D nanomaterials.

cond-mat.mtrl-sci↗

arXiv2023

Data-driven path collective variables

Identifying optimal collective variables to model transformations, using atomic-scale simulations, is a long-standing challenge. We propose a new method for the generation, optimization, and comparison of collective variables, which can be thought of as a data-driven generalization of the path collective variable concept. It consists in a kernel ridge regression of the committor probability, which encodes a transformation's progress. The resulting collective variable is one-dimensional, interpretable, and differentiable, making it appropriate for enhanced sampling simulations requiring biasing. We demonstrate the validity of the method on two different applications: a precipitation model, and the association of Li$^+$ and F$^-$ in water. For the former, we show that global descriptors such as the permutation invariant vector allow to reach an accuracy far from the one achieved \textit{via} simpler, more intuitive variables. For the latter, we show that information correlated with the transformation mechanism is contained in the first solvation shell only, and that inertial effects prevent the derivation of optimal collective variables from the atomic positions only.

physics.chem-ph↗

arXiv2023

Optimal reaction coordinates and kinetic rates from the projected dynamics of transition paths

Finding optimal reaction coordinates and predicting accurate kinetic rates for activated processes are two of the foremost challenges of molecular simulations. We introduce an algorithm that tackles the two problems at once: starting from a limited number of reactive molecular dynamics trajectories (transition paths), we automatically generate with a Monte Carlo approach a sequence of different reaction coordinates that progressively reduce the kinetic rate of their projected effective dynamics. Based on a variational principle, the minimal rate accurately approximates the exact one, and it corresponds to the optimal reaction coordinate. After benchmarking the method on an analytic double-well system, we apply it to complex atomistic systems: the interaction of carbon nanoparticles of different sizes in water.

cond-mat.stat-mech↗

arXiv2023

A critical comparison of general-purpose collective variables for crystal nucleation

The nucleation of crystals is a prominent phenomenon in science and technology that still lacks a full atomic-scale understanding. Much work has been devoted to identifying order parameters able to track the process, from the inception of early nuclei to their maturing to critical size until growth of an extended crystal. We critically assess and compare two powerful distance-based collective variables, an effective entropy derived from liquid state theory and the path variable based on permutation invariant vectors using the Kob-Andersen binary mixture and a combination of enhanced-sampling techniques. Our findings reveal a comparable ability to drive nucleation when a bias potential is applied, and comparable free-energy barriers and structural features. Yet, we also found an imperfect correlation with the committor probability on the barrier top which was bypassed by changing the order parameter definition.

physics.comp-ph↗

arXiv2022

Free Energy Landscapes, Diffusion Coefficients, and Kinetic Rates from Transition Paths

We address the problem of constructing accurate mathematical models of the dynamics of complex systems projected on a collective variable. To this aim we introduce a conceptually simple yet effective algorithm for estimating the parameters of Langevin and Fokker-Planck equations from a set of short, possibly out-of-equilibrium molecular dynamics trajectories, obtained for instance from transition path sampling or as relaxation from high free-energy configurations. The approach maximizes the model likelihood based on any explicit expression of the short-time propagator, hence it can be applied to different evolution equations. We demonstrate the numerical efficiency and robustness of the algorithm on model systems, and we apply it to reconstruct the projected dynamics of pairs of C60 and C240 fullerene molecules in explicit water. Our methodology allows reconstructing the accurate thermodynamics and kinetics of activated processes, namely free energy landscapes, diffusion coefficients, and kinetic rates. Compared to existing enhanced sampling methods, we directly exploit short unbiased trajectories at a competitive computational cost.

cond-mat.stat-mech↗

arXiv2022

Likelihood-based non-Markovian models from molecular dynamics

We introduce a new method to accurately and efficiently estimate the effective dynamics of collective variables in molecular simulations. Such reduced dynamics play an essential role in the study of a broad class of processes, ranging from chemical reactions in solution to conformational changes in biomolecules or phase transitions in condensed matter systems. The standard Markovian approximation often breaks down due to the lack of a proper separation of time scales and memory effects must be taken into account. Using a parametrization based on hidden auxiliary variables, we obtain a generalized Langevin equation by maximizing the statistical likelihood of the observed trajectories. Both the memory kernel and random noise are correctly recovered by this procedure. This data-driven approach provides a reduced dynamical model for multidimensional collective variables, enabling the accurate sampling of their long-time dynamical properties at a computational cost drastically reduced with respect to all-atom numerical simulations. The present strategy, based on the reproduction of the dynamics of trajectories rather than the memory kernel or the velocity-autocorrelation function, conveniently provides other observables beyond these two, including e.g. stationary currents in non-equilibrium situations, or the distribution of first passage times between metastable states.

cond-mat.stat-mech↗

arXiv2021

Transition rates, survival probabilities, and quality of bias from time-dependent biased simulations

Simulations with an adaptive time-dependent bias, such as metadynamics, enable an efficient exploration of the conformational space of a system. However, the dynamic information of the system is altered by the bias. With infrequent metadynamics it is possible to recover the transition rate of crossing a barrier, if the collective variables are ideal and there is no bias deposition near the transition state. Unfortunately, for simulations of complex molecules, these conditions are not always fulfilled. To overcome these limitations, and inspired by single-molecule force spectroscopy, we developed a method based on Kramers' theory for calculating the barrier-crossing rate when a time-dependent bias is added to the system. We assess the quality of the bias parameter by measuring how efficiently the bias accelerates the transitions compared to ideal behavior. We present approximate analytical expressions of the survival probability that accurately reproduce the barrier-crossing time statistics, and enable the extraction of the unbiased transition rate even for challenging cases, where previous methods fail.

physics.bio-ph↗

arXiv2021

Free energy calculations and unbiased dynamics reveal a continuous liquid-liquid transition in water no man's land

The existence of a first-order phase transition between a low-density liquid (LDL) and a high-density liquid (HDL) forms of supercooled water has been a central and highly debated issue of physics and chemistry in the last three decades. We present a computational study that allows to determine the free-energy landscapes of supercooled water over a wide range of pressure and temperature conditions, using the accurate TIP4P/2005 force field. Our approach combines topology-based structural transformation coordinates, state-of-the-art free-energy calculation methods, and extensive unbiased molecular dynamics simulations. All our results indicate that transitions between the LDL and HDL forms are smooth throughout the so-called "no man's land", and that free-energy barriers do not appear until the onset of the solid, non-diffusive amorphous forms.

cond-mat.soft↗

arXiv2020

Out-of-equilibrium Polymorph Selection in Nanoparticle Freezing

The ability to design synthesis processes that are out of equilibrium has opened the possibility of creating nanomaterials with remarkable physico-chemical properties, choosing from a much richer palette of possible atomic architectures compared to equilibrium processes in extended systems. In this work, we employ atomistic simulations to demonstrate how to control polymorph selection via cooling rate during nanoparticle freezing in the case of Ni$_3$Al, a material with a rich structural landscape. State-of-the-art free-energy calculations allow to rationalize the complex nucleation process, discovering a switch between two kinetic pathways, yielding the equilibrium structure at room temperature and an alternative metastable one at higher temperature. Our findings address the key challenge in the synthesis of nano-alloys for technological applications, i.e., rationally exploiting the competition between kinetics and thermodynamics by designing a treatment history that forces the system into desirable metastable states.

cond-mat.mtrl-sci↗

arXiv2020

Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems

Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

physics.comp-ph↗

arXiv2018

Free energy, friction, and mass profiles from short molecular dynamics trajectories

We address the problem of constructing accurate mathematical models of the dynamics of molecular systems projected on a collective variable. To this aim we introduce an algorithm optimizing the parameters of a standard or generalized Langevin equation until the latter reproduces in a faithful way a set of molecular dynamics trajectories. In particular, using solvated proline dipeptide as a test case, we report evidence that ~100 short trajectories initiated at the top of a high barrier encode all the information needed to reconstruct free energy, friction, and mass profiles, including non-Markovian effects. The approach allows accessing the thermodynamics and kinetics of activated processes in a conceptually direct way, it employs only standard unbiased molecular dynamics trajectories, and is competitive in computational cost with respect to existing enhanced sampling methods. Furthermore, the systematic construction of Langevin models for different choices of collective variables starting from the same initial data could help in reaction coordinate optimization.

cond-mat.stat-mech↗

arXiv2018

Navigating at Will on the Water Phase Diagram

Despite the simplicity of its molecular unit, water is a challenging system because of its uniquely rich polymorphism and predicted but yet unconfirmed features. Introducing a novel space of generalized coordinates that capture changes in the topology of the interatomic network, we are able to systematically track transitions among liquid, amorphous and crystalline forms throughout the whole phase diagram of water, including the nucleation of crystals above and below the melting point. Our approach, based on molecular dynamics and enhanced sampling / free energy calculation techniques, is not specific to water and could be applied to very different structural phase transitions, paving the way towards the prediction of kinetic routes connecting polymorphic structures in a range of materials.

cond-mat.mtrl-sci↗

arXiv2008

Ab initio study of the vibrational properties of crystalline TeO2: The alpha, beta, and gamma phases

Based on density functional perturbation theory, we have studied the vibrational properties of three crystalline phases of tellurium dioxide: paratellurite alpha-TeO2, tellurite beta-TeO2, and the new phase gamma-TeO2, recently identified experimentally. Calculated Raman and IR spectra are in good agreement with available experimental data. The vibrational spectra of alpha- and beta-TeO2 can be interpreted in terms of vibrations of TeO2 molecular units.

cond-mat.mtrl-sci↗