Learning Matter: Materials Design with Machine Learning and Atomistic Simulations-Reference-Cited by-同舟云学术

Learning Matter: Materials Design with Machine Learning and Atomistic Simulations

Published:2022-02-21 Issue:3 Volume:3 Page:343-357
ISSN:2643-6728
Container-title:Accounts of Materials Research
language:en
Short-container-title:Acc. Mater. Res.

Author:

Axelrod Simon¹,Schwalbe-Koda Daniel^ORCID,Mohapatra Somesh^ORCID,Damewood James,Greenman Kevin P.^ORCID,Gómez-Bombarelli Rafael^ORCID

Affiliation:

1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States

Funder

Defense Advanced Research Projects Agency

Novo Nordisk

Division of Graduate Education

Advanced Research Projects Agency - Energy

Massachusetts Institute of Technology

U.S. Department of Defense

Google

Takeda Pharmaceuticals Company Limited

Jameel Clinic, Massachusetts Institute of Technology

MIT-IBM Watson AI Lab

Zapata Computing

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Polymers and Plastics,Materials Science (miscellaneous),Chemical Engineering (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/accountsmr.1c00238

Reference60 articles.

1. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

2. Data-driven modeling and learning in science and engineering

3. Aspuru-Guzik, A.; Persson, K. Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods and Artificial Intelligence. Mission Innovation 2018, 6, http://nrs.harvard.edu/urn-3:HUL.InstRepos:35164974 (accessed 2022-02-18).

4. The 2019 materials by design roadmap

5. Machine learning: Accelerating materials development for energy storage and conversion

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