A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters-Reference-Cited by-同舟云学术

A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

Published:2020-07-14 Issue:2 Volume:153 Page:024302
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Bilbrey Jenna A.¹,Heindel Joseph P.²,Schram Malachi³,Bandyopadhyay Pradipta⁴,Xantheas Sotiris S.²³^ORCID,Choudhury Sutanay³

Affiliation:

1. Computing and Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, USA

2. Department of Chemistry, University of Washington, Seattle, Washington 98195, USA

3. Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, Richland, Washington 99352, USA

4. School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India

Funder

US Department of Energy, Exascale Computing Project, ExaLearn Co-Design Center

US Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences

DST-PURSE, UPE-II and JNU, India

National Energy Research Supercomputing Center, Supported by the Office of Science, US Department of Energy

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0009933

Reference52 articles.

1. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

2. Inverse molecular design using machine learning: Generative models for matter engineering

3. Machine Learning in Computer-Aided Synthesis Planning

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