Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment-Reference-Cited by-同舟云学术

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

Author:

Bass Lewis¹,Elder Luke H.²,Folescu Dan E.²³,Forouzesh Negin⁴^ORCID,Tolokh Igor S.²,Karpatne Anuj²,Onufriev Alexey V.²⁵⁶^ORCID

Affiliation:

1. Department of Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States

2. Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States

3. Department of Mathematics, Virginia Tech, Blacksburg, Virginia 24061, United States

4. Department of Computer Science, California State University, Los Angeles, California 90032, United States

5. Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States

6. Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States

Funder

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

Reference144 articles.

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