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Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

Lei Zhang1*, Gábor Csányi2, Erik van der Giessen3, Francesco Maresca1*

1 Engineering and Technology Institute, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands

2 Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom

3 Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands

* Corresponding authors emails: lei.zhang@rug.nl, f.maresca@rug.nl
DOI10.24435/materialscloud:ps-p7 [version v1]

Publication date: Aug 11, 2022

How to cite this record

Lei Zhang, Gábor Csányi, Erik van der Giessen, Francesco Maresca, Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential, Materials Cloud Archive 2022.102 (2022), doi: 10.24435/materialscloud:ps-p7.

Description

Existing, classical interatomic potentials for bcc iron predict contradicting crack-tip mechanisms (i.e. cleavage, dislocation emission, phase transition) for the same crack systems, thus leaving the crack propagation mechanism in bcc iron unclear. In this work, we develop a Gaussian approximation potential (GAP) by extending a DFT database for ferromagnetic bcc iron to include highly distorted primitive bcc cells and surface separation, along with small crack-tip configurations that are identified by means of a fully automated active learning workflow. Our GAP (referred to as Fe-GAP22) predicts crack propagation within 8 meV/atom accuracy. The fully automated, active learning workflow is made publicly available on GitHub. With the newly developed Fe-GAP22, we find that in absence of other defects around the crack tip (e.g. nanovoids, dislocations), the static (T=0K) crack-tip mechanism is cleavage, thus settling the contradictions in the literature. Our work also highlights the need for multi-scale modelling to predict fracture at finite temperatures and finite strain rates.

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Files

File name Size Description
GAP22.zip
MD5md5:8a370b53981b2fe7caba6f13a46da1d4
4.4 MiB GAP potential file and DFT database
train.in
MD5md5:b923b10134658e8f7c36960ca5bad9a4
2.2 KiB QUIP training commands of current Fe-GAP22

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

Keywords

Fracture Gaussian Approximation Potential bcc iron

Version history:

2022.102 (version v1) [This version] Aug 11, 2022 DOI10.24435/materialscloud:ps-p7