Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Advanced Engineering Materials, vol. 24

Abstract

Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small‐scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain, and partitioning of stress and strain fields in individual grains. Herein, a methodology is developed using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. Three important field variables are focused on: total strain, elastic strain, and microrotation. The results show that the elastic strain in individual grains exhibits a unimodal lognormal distribution, while the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, the identified peaks are evaluated in terms of deformation mechanisms in a grain, which, e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models.

Authors 2

1. Aruna Prakash corresponding

   TU Bergakademie Freiberg

   Affiliation as printed

   Micromechanical Materials Modelling Group (MiMM) Institute of Mechancis and Fluid Dynamics Technical University Bergakademie Freiberg Lampadiusstraße 4 09599 Freiberg Germany

2. Stefan Sandfeld Aachen Chair of Materials Data Science and Materials Informatics Faculty 5—Georesources and Materials Engineering

   RWTH Aachen University · Forschungszentrum Jülich · TU Bergakademie Freiberg

   Affiliation as printed

   Chair of Materials Data Science and Materials Informatics Faculty 5—Georesources and Materials Engineering RWTH Aachen University 52056 Aachen Germany

   Institute for Advanced Simulation—IAS-9 Materials Data Science and Informatics Forschungszentrum Juelich GmbH 52425 Zurich Germany

   Micromechanical Materials Modelling Group (MiMM) Institute of Mechancis and Fluid Dynamics Technical University Bergakademie Freiberg Lampadiusstraße 4 09599 Freiberg Germany