Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation-Reference-Cited by-同舟云学术

Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation

Published:2021-04-07 Issue:13 Volume:154 Page:134113
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Weinreich Jan¹^ORCID,Browning Nicholas J.²^ORCID,von Lilienfeld O. Anatole¹²^ORCID

Affiliation:

1. University of Vienna, Faculty of Physics, Kolingasse 14-16, AT-1090 Wien, Austria

2. Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland

Funder

Swiss National Science Foundation

FP7 Ideas: European Research Council

H2020 European Research Council

National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0041548

Reference68 articles.

1. Making free-energy calculations routine: Combining first principles with machine learning

2. Machine Learning-Guided Approach for Studying Solvation Environments

3. The adaptive buffered force QM/MM method in the CP2K and AMBER software packages

4. Calculation of solvation free energy utilizing a constrained QM/MM approach combined with a theory of solutions

5. The Poisson-Boltzmann equation for biomolecular electrostatics: a tool for structural biology

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