arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2206.08048cond-mat.mtrl-scics.LG
收藏

Automated analysis of continuum fields from atomistic simulations using statistical machine learning

Technical University Bergakademie Freiberg(弗赖贝格工业大学);Forschungszentrum Juelich GmbH(于利希研究中心);RWTH Aachen University(亚琛工业大学)

Aruna Prakash, Stefan Sandfeld

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑