Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning-Reference-Cited by-同舟云学术

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Published:2021 Issue:45 Volume:12 Page:14987-15006
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Li Wei¹^ORCID,Ma Haibo¹²^ORCID,Li Shuhua¹^ORCID,Ma Jing¹²^ORCID

Affiliation:

1. Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China

2. Jiangsu Key Laboratory of Advanced Organic Materials, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University, Nanjing 210023, China

Abstract

Low scaling quantum mechanics calculations and machine learning can be employed to efficiently predict the molecular energies, forces, and optical and electrical properties of molecular materials and their aggregates.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D1SC02574K

Reference218 articles.

1. The high-throughput highway to computational materials design

2. Integrated computational materials engineering for advanced materials: A brief review

3. Computational Design of Functional Materials

4. Computational predictions of energy materials using density functional theory

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