Javier Fernandez Troncoso¹, Yang Hu², Nicolo Maria della Ventura³, Amit Sharma³, Xavier Maeder³, Vladyslav Turlo^1*

1 Laboratory for Advanced Materials Processing, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland

2 Mechanics and Materials Lab, Department of Mechanical and Process Engineering, ETH Zurich, 8092, Zurich, Switzerland

3 Laboratory for Mechanics of Materials and Nanostructures, Empa - Swiss Federal Laboratories for Materials Science and Technology, CH-3603 Thun, Bern, Switzerland

* Corresponding authors emails: vladyslav.turlo@empa.ch

DOI10.24435/materialscloud:aj-wq [version v1]

Publication date: Mar 15, 2023

How to cite this record

Javier Fernandez Troncoso, Yang Hu, Nicolo Maria della Ventura, Amit Sharma, Xavier Maeder, Vladyslav Turlo, Machine learning of twin/matrix interfaces from local stress field, Materials Cloud Archive 2023.43 (2023), doi: 10.24435/materialscloud:aj-wq.

Description

Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.

Materials Cloud sections using this data

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File name	Size	Description
README.txt MD5md5:df6478017a8f7bbe8d925448cb37d9d4	369 Bytes	README file
data.7z MD5md5:6d9d71d1842ded7d06c8757d8d84d806	550.0 MiB	Data supporting the publication

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External references

Keywords

machine learning molecular dynamics magnesium twinning MARVEL/DD1