"Machine learning to tame divergent density functional approximations: a ..."

Chenru Duan et al. (2021)

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DOI: —

access: open

type: Informal or Other Publication

metadata version: 2021-07-01

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  - electronic edition @ arxiv.org (open access)
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  dblp key:
  - journals/corr/abs-2106-13109
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  persistent URL:
  - https://dblp.org/rec/journals/corr/abs-2106-13109
Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik:
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. CoRR abs/2106.13109 (2021)

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