CUED Publications database

Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"

Bernstein, N and Csanyi, G and Deringer, V Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces". (Unpublished)

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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: