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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning

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Author(s):
Schleder, Gabriel R. [1, 2] ; Acosta, Carlos Mera [1] ; Fazzio, Adalberto [1, 2]
Total Authors: 3
Affiliation:
[1] Fed Univ ABC UFABC, BR-09210580 Santo Andre, SP - Brazil
[2] Brazilian Nanotechnol Natl Lab LNNano CNPEM, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: ACS APPLIED MATERIALS & INTERFACES; v. 12, n. 18, p. 20149-20157, MAY 6 2020.
Web of Science Citations: 2
Abstract

The increasing interest and research on two-dimensional (2D) materials has not yet translated into a reality of diverse materials applications. To go beyond graphene and transition metal dichalcogenides for several applications, suitable candidates with desirable properties must be proposed. Here we use machine learning techniques to identify thermodynamically stable 2D materials, which is the first essential requirement for any application. According to the formation energy and energy above the convex hull, we classify materials as having low, medium, or high stability. The proposed approach enables the stability evaluation of novel 2D compounds for further detailed investigation of promising candidates, using only composition properties and structural symmetry, without the need for information about atomic positions. We demonstrate the usefulness of the model generating more than a thousand novel compounds, corroborating with DFT calculations the classification for five of these materials. To illustrate the applicability of the stable materials, we then perform a screening of electronic materials suitable for photoelectrocatalytic water splitting, identifying the potential candidate Sn2SeTe generated by our model, and also PbTe, both not yet reported for this application. (AU)

FAPESP's process: 18/11856-7 - Interface-induced effects in quantum materials
Grantee:Carlos Augusto Mera Acosta
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 17/18139-6 - Machine learning for Materials Science: 2D materials discovery and design
Grantee:Gabriel Ravanhani Schleder
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties
Grantee:Adalberto Fazzio
Support Opportunities: Research Projects - Thematic Grants