Journal Article

FZJ-2023-00981

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Prakash, A. (Corresponding author)Extern* ; Sandfeld, S.FZJ*

2022
Deutsche Gesellschaft für Materialkunde Frankfurt, M.

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Please use a persistent id in citations: http://hdl.handle.net/2128/33793  doi:10.1002/adem.202200574

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.

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Contributing Institute(s):

1. Materials Data Science and Informatics (IAS-9)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. MuDiLingo - A Multiscale Dislocation Language for Data-Driven Materials Science (759419) (759419)

Appears in the scientific report 2022

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Wiley ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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