Research data supporting "De novo exploration and self-guided learning of potential-energy surfaces"
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下载链接：
https://www.repository.cam.ac.uk/handle/1810/297743
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资源简介：
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.
提供机构：
Apollo - University of Cambridge Repository
创建时间：
2019-09-03
