Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms-Reference-Cited by-同舟云学术

Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms

Published:2022 Issue:46 Volume:24 Page:28314-28324
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Ho I-Ting¹,Matysik Milena²,Herrera Liliana Montano³,Yang Jiyoung⁴,Guderlei Ralph Joachim²,Laussegger Michael⁵,Schrantz Bernhard⁵,Hammer Regine⁶,Miranda-Quintana Ramón Alain⁷^ORCID,Smiatek Jens⁸⁹^ORCID

Affiliation:

1. Boehringer Ingelheim Pharma GmbH & Co. KG, Global Computational Biology and Digital Sciences, D-88397 Biberach (Riss), Germany

2. Boehringer Ingelheim GmbH & Co. KG, IT RDM Services, D-88397 Biberach (Riss), Germany

3. Boehringer Ingelheim Pharma GmbH & Co. KG, Global Innovation & Alliance Management, D-88397 Biberach (Riss), Germany

4. Boehringer Ingelheim Pharma GmbH & Co. KG, Analytical Development Biologicals, D-88397 Biberach (Riss), Germany

5. Boehringer Ingelheim RCV GmbH & Co. KG, IT RDM Services, A-1121 Vienna, Austria

6. Boehringer Ingelheim GmbH & Co. KG, OneHP Business Unit, D-88397 Biberach (Riss), Germany

7. Department of Chemistry and Quantum Theory Project, University of Florida, FL 32611, USA

8. Boehringer Ingelheim GmbH & Co. KG, Development NCE, D-88397 Biberach (Riss), Germany

9. Institute for Computational Physics, University of Stuttgart, Allmandring 3, D-70569 Stuttgart, Germany

Abstract

We present explainable machine learning approaches for understanding and predicting free energies, enthalpies, and entropies of ion pairing in different solvents.

Funder

University of Florida

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP04428E

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