arXiv subjects

Mauro Pulzone

Publications and source records attributed to Mauro Pulzone.

2 recordsLinked to original sources

arXiv2026

Effective dynamic constants for nonequilibrium third-principles simulations

Computational studies of the thermodynamic properties of materials at the mesoscopic and macroscopic scales -- involving lengths and times of at least $\mu$m and $\mu$s, respectively -- rely on a coarse-graining approximation such that only a few relevant collective variables are treated explicitly. Those variables typically take the form of fields defined everywhere in space or macroscopic quantities when spatial inhomogeneities can be treated implicitly. The free energy is usually expressed as a Landau-like potential whose temperature-dependent minima track stable states, characteristic equilibrium fluctuations being implicitly accounted for. Further, the response of the system to external perturbations, and its relaxation toward thermal equilibrium, are described in terms of simple equations of motion governed by effective inertial and viscous-damping constants. There is considerable literature on the problem of deriving Landau free energy potentials, from either experiment or predictive atomistic simulations, including recent efforts to develop systematic machine-learning approaches that we denote ``third principles''. Much less attention has received the calculation of the effective constants controlling the nonequilibrium macroscopic or mesoscopic dynamics. Here we tackle that problem, describing a protocol that allows us to compute the temperature-dependent inertial and damping coefficients associated to the electric polarization in representative soft-mode ferroelectric PbTiO$_{3}$. Our scheme lends itself to a widespread application, although the non-trivial behaviors found in PbTiO$_{3}$ suggest that more case studies will be needed to finetune a general and robust calculation protocol. Our results also allow us to comment on common assumptions in the literature of effective dynamic treatments of ferroelectrics and related materials.

cond-mat.mtrl-sci↗

arXiv2025

Machine learning Landau free energy potentials

We show how to construct Landau-like free energy potentials using a machine-learning approach. For concreteness, we focus on perovskite oxide PbTiO$_{3}$. We work with a training set obtained from Monte Carlo simulations based on an atomistic ''second-principles'' potential for PbTiO$_{3}$. We rely exclusively on data that would be experimentally accessible -- i.e., temperature-dependent polarization and strain, both with and without external electric fields and stresses applied --, to explore scenarios where the training set could be obtained from laboratory measurements. We introduce a scheme that allows us to identify optimal polynomial models of the temperature-dependent free energy surface, mapped as a function of the homogeneous electric polarization and homogeneous strain. Our results for PbTiO$_{3}$ show that a very simple polynomial -- where only two parameters depend linearly on temperature -- is sufficient to yield a correct description of the material's behavior. Remarkably, the obtained models also capture the subtle couplings by which elastic strain controls key features of ferroelectricity in PbTiO$_{3}$ -- i.e., the symmetry of the polar phase and the discontinuous character of the transition --, despite the fact that no effort was made to include such information in the training set. We emphasize the distinctive aspects of our methodology (which relies on an original form of validation step) by comparing it with the usual machine-learning approach for model construction. Our results illustrate how physically motivated models can have remarkable predictive power, even if they are derived from a limited amount of data. We argue that such ''third-principles'' models can be the basis for predictive macroscopic or mesoscopic simulations of ferroelectrics and other materials undergoing non-reconstructive structural transitions.

cond-mat.mtrl-sci↗