Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions-Reference-Cited by-同舟云学术

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Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions

Published:2024-07-18 Issue:15 Volume:20 Page:6756-6771
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Du Hongwei¹²³,Hui Jian¹²³,Zhang Lanting¹²³^ORCID,Wang Hong¹²³^ORCID

Affiliation:

1. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

2. Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201203, China

3. Materials Genome Initiative Center, Shanghai Jiao Tong University, Shanghai 200240, China

Funder

National Key Research and Development Program of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.4c00187

Reference59 articles.

1. Inhomogeneous Electron Gas

2. Self-Consistent Equations Including Exchange and Correlation Effects

3. Accelerating the discovery of materials for clean energy in the era of smart automation

4. Big Data of Materials Science: Critical Role of the Descriptor

5. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

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