Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion-Reference-Cited by-同舟云学术

Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion

Published:2020-01-21 Issue:3 Volume:11 Page:981-985
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Jirasek Fabian¹²^ORCID,Alves Rodrigo A. S.³,Damay Julie⁴,Vandermeulen Robert A.³,Bamler Robert¹,Bortz Michael⁴^ORCID,Mandt Stephan¹,Kloft Marius³,Hasse Hans²

Affiliation:

1. Department of Computer Science, University of California, Irvine, California 92697, United States

2. Laboratory of Engineering Thermodynamics (LTD), TU Kaiserslautern, 67663 Kaiserslautern, Germany

3. Machine Learning Group, Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany

4. Fraunhofer Institute for Industrial Mathematics ITWM, 67663 Kaiserslautern, Germany

Funder

Defense Advanced Research Projects Agency

Deutscher Akademischer Austauschdienst

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Fraunhofer-Gesellschaft

Qualcomm

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.9b03657

Reference32 articles.

1. The promise of artificial intelligence in chemical engineering: Is it here, finally?

2. Machine learning in materials informatics: recent applications and prospects

3. Machine learning for molecular and materials science

4. Machine learning methods in chemoinformatics

5. Predicting infinite dilution activity coefficients of organic compounds in water by quantum-connectivity descriptors

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