Jarrell, M. | Engineering Papers

DOE OSTI2020

Ab initio typical medium theory of substitutional disorder

Here, by merging single-site typical medium theory with density-functional theory, we introduce a self-consistent framework for electronic-structure calculations of materials with substitutional disorder which takes into account Anderson localization. The scheme and details of the implementation are presented and applied to the hypothetical alloy Li$_cBe_{1-c}$, and the results are compared with those obtained with the coherent potential approximation. Furthermore, we demonstrate that Anderson localization suppresses ferromagnetic order for a very low concentration of (i) carbon impurities substituting oxygen in $MgO_{1-c}C_c$ and (ii) manganese impurities substituting magnesium in $Mg_{1-c}Mn_cO$ for the low-spin magnetic configuration.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Identifying structural changes with unsupervised machine learning methods

In this work, unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, k-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems.

42 ENGINEERING↗