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자연과학대학
물리학전공
Journal papers
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Machine-learning structural reconstructions for accelerated point defect calculations
Title
Machine-learning structural reconstructions for accelerated point defect calculations
Authors
Mosquera-Lois
;
Irea
;
Kavanagh
;
Seán R.
;
Ganose
;
Alex M.
;
Walsh
;
Aron
Ewha Authors
Aron Walsh
SCOPUS Author ID
Aron Walsh
Issue Date
2024
Journal Title
npj Computational Materials
ISSN
2057-3960
Citation
npj Computational Materials vol. 10, no. 1
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the structural landscape of neutral point defects. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSexTe1−x alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurationally complex systems. © The Author(s) 2024.
DOI
10.1038/s41524-024-01303-9
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