  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. In this work we develop a methodology using statistical
data mining and machine learning algorithms to automate the analysis of
continuum field variables in atomistic simulations. We focus on three important
field variables: total strain, elastic strain and microrotation. Our results
show that the elastic strain in individual grains exhibits a unimodal
log-normal distribution, whilst 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, we evaluate the identified
peaks 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.

Materials Science;Machine Learning