Journal
ACS APPLIED MATERIALS & INTERFACES
Volume 12, Issue 18, Pages 20149-20157Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsami.9b14530
Keywords
two-dimensional materials; machine learning; high throughput screening; big data; density functional theory (DFT)
Funding
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [17/18139-6, 18/11856-7, 17/02317-2]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [18/11856-7] Funding Source: FAPESP
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available