Miguel Spinola

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Spinola

First Name

Miguel

ORCID

0000-0002-5180-6149

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Publications 1 - 4 of 4

Finite-temperature grain boundary properties from quasistatic atomistics
Spinola, Miguel; Saxena, Shashank; Gupta, Prateek; et al. (2024)
Computational Materials Science
Grain boundary (GB) properties greatly influence the mechanical, electrical, and thermal response of polycrystalline materials. Most computational studies of GB properties at finite temperatures use molecular dynamics (MD), which is computationally expensive, limited in the range of accessible timescales, and requires cumbersome techniques like thermodynamic integration to estimate free energies. This restricts the reasonable computation (without incurring excessive computational expense) of GB properties to regimes that are often unrealistic, such as zero temperature or extremely high strain rates. Consequently, there is a need for simulation methodology that avoids the timescale limitations of MD, while providing reliable estimates of GB properties. The Gaussian Phase-Packet (GPP) method is a temporal coarse-graining technique that can predict relaxed atomic structures at finite temperature in the quasistatic limit. This work applies GPP, combined with the quasiharmonic approximation for computing the free energy, to the problem of determining the free energy and shear coupling factor of grain boundaries in metals over a range of realistic temperatures. Validation is achieved by comparison to thermodynamic integration and quasiharmonic approximation (QHA), which confirms that the presented approach captures relaxed-energy GB structures and shear coupling factors at finite temperature with a high degree of accuracy, and it performs significantly better than QHA on hydrostatically expanded 0 K structures.
GNN-assisted phase space integration with application to atomistics
Saxena, Shashank; Bastek, Jan-Hendrik; Spinola, Miguel; et al. (2023)
Mechanics of Materials
Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations---especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, we highlight the impact on time-coarsened atomistic predictions, and we discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics.
AQCNES: A Quasi-Continuum Non-Equilibrium Solver
Bräunlich, Gerhard; Saxena, Shashank; Weberndorfer, Manuel; et al. (2024)
Journal of Open Source Software
The behavior of macroscopic structures is determined by fast atomic interactions at the nanoscales. Current atomic simulation techniques, such as molecular dynamics (MD), are limited to a millions of atoms and hence a few micrometers of domain length. Moreover, finite temperature vibrational frequencies of around tens of terahertz restrict the time step of MD to femtoseconds, precluding the simulation of problems of engineering interest. Consequently, there has been a significant focus in recent decades on developing multiscale modeling techniques to extend atomistic accuracy to larger length scales and longer time frames. Existing techniques, such as the quasicontinuum (QC) method, are restricted to spatial upscaling at zero temperature, while temporal upscaling methods like the maximum entropy (max-ent) approach are constrained to fully resolved atomistic simulations at finite temperature.
Predicting temperature-dependent failure and transformation zones in 2D silica glass through quasistatic Gaussian Phase Packets
Spinola, Miguel; Saxena, Shashank; Bamer, Franz; et al. (2026)
Computer Methods in Applied Mechanics and Engineering
The athermal quasistatic (AQS) method is a powerful technique to study the mechanical behavior of disordered systems. However, its applicability is limited to temperatures near zero, where thermal activation is unlikely. In this work, we extend the AQS method to finite temperatures, based on a formulation that describes atoms as temperature-dependent Gaussian packets (GPPs) in phase space under quasistatic conditions, equivalent to minimum free energy conditions. This framework is used to study the effect of temperature on the onset of inelasticity and fracture in amorphous two-dimensional silica glass approaching quasistatic conditions under uniaxial tensile loading. An important characteristic of this formulation is the directional dependence of the variance of each Gaussian packet in configuration space, making this formulation an inexpensive and accurate predictor of zones prone to atomic-scale rearrangements, both in the undeformed state and (with increasing accuracy) as the deformation progresses. This method is also shown to accurately capture the thermal expansion of the disordered material. Furthermore, combining the GPP description with Metropolis sampling predicts the effect of temperature on the onset of fracture of the material, which is validated through MD simulations at strain rates as low as 10⁴s⁻¹. The presented framework provides a valuable technique for studying the nonlinear mechanics of disordered materials at finite temperature and for predicting local rearrangement zones in disordered solids efficiently without the need for expensive MD simulations.

Publications 1 - 4 of 4

Miguel Spinola

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