Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models-Reference-Cited by-同舟云学术

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Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models

Published:2023-07-18 Issue:29 Volume:14 Page:6630-6638
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Wines Daniel¹^ORCID,Xie Tian²,Choudhary Kamal¹³^ORCID

Affiliation:

1. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States

2. Microsoft Research AI4Science, Cambridge, United Kingdom CB1 2FB

3. DeepMaterials LLC, Silver Spring, Maryland 20906, United States

Funder

National Institute of Standards and Technology

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.3c01260

Reference68 articles.

1. 100 Years of Superconductivity

2. BCS: 50 Years

3. Electron-phonon interactions from first principles

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