Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms-Reference-Cited by-同舟云学术

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Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms

Published:2020-06-15 Issue:28 Volume:124 Page:5804-5811
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Dandu Naveen¹²^ORCID,Ward Logan²³^ORCID,Assary Rajeev S.¹²^ORCID,Redfern Paul C.¹,Narayanan Badri⁴^ORCID,Foster Ian T.²³⁵,Curtiss Larry A.¹²^ORCID

Affiliation:

1. Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

2. Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, Illinois 60439, United States

3. Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

4. Department of Mechanical Engineering, University of Louisville, Louisville, Kentucky 40292, United States

5. Department of Computer Science, University of Chicago, Chicago, Illinois 60637, United States

Funder

Basic Energy Sciences

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.0c01777

Reference50 articles.

1. Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening

2. Chemical space

3. Combinatorial thinking in chemistry and biology

5. Gaussian-4 theory using reduced order perturbation theory

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