Aruna Prakash

Aruna Prakash, Stefan Sandfeld

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.

M. Vimal, S. Sandfeld, A. Prakash

Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of results.