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Machine learning-based discovery of vibrationally stable materials
journal contribution
posted on 2023-02-20, 03:57 authored by Sherif AbbasSherif Abbas, M Rashid, S Gupta, SP Russo, Tiffany WalshTiffany Walsh, Svetha VenkateshSvetha VenkateshThe identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.
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npj Computational MaterialsVolume
9Article number
ARTN 5Publisher DOI
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2057-3960eISSN
2057-3960Language
EnglishPublication classification
C1 Refereed article in a scholarly journalIssue
1Publisher
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