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

arXiv: https://arxiv.org/abs/2206.08048 · HTML: https://mlsanity.com/paper/2206.08048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 76 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships](https://mlsanity.com/paper/2007.13417/) (2020)
- [OptiMic: A tool to generate optimized polycrystalline microstructures for materials simulations](https://mlsanity.com/paper/2107.02460/) (2021)
- [Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis](https://mlsanity.com/paper/2112.03348/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials](https://mlsanity.com/paper/2309.06613/) (2023)

## Abstract (arXiv)

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.