Bernstein, N and Csanyi, G and Deringer, V Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces". (Unpublished)
Full text not available from this repository.Abstract
This dataset supports our work on Gaussian Approximation Potential driven random structure searching (GAP-RSS) models for exploring and fitting potential-energy surfaces of materials. It provides, in separate tar archives, an implementation of the methodology and the final GAP-RSS models as reported in the associated publication.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | density functional theory machine learning |
| Subjects: | UNSPECIFIED |
| Divisions: | Div C > Applied Mechanics |
| Depositing User: | Cron Job |
| Date Deposited: | 04 Sep 2019 20:03 |
| Last Modified: | 18 Feb 2021 15:51 |
| DOI: |
