E(<i>n</i>) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules-Reference-Cited by-同舟云学术

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E(n) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules

Published:2024-01-17 Issue:4 Volume:128 Page:1108-1117
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Nehil-Puleo Kieran¹,Quach Co D.²,Craven Nicholas C.¹,MCabe Clare²³^ORCID,Cummings Peter T.¹²³

Affiliation:

1. Interdisciplinary Material Science Program, Vanderbilt University, Nashville, Tennessee 37235, United States

2. Department of Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, Tennessee 37235-1826, United States

3. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, Scotland, U.K.

Funder

Vanderbilt University

Engineering and Physical Sciences Research Council

Office of Advanced Cyberinfrastructure

Division of Chemical, Bioengineering, Environmental, and Transport Systems

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.3c07304

Reference49 articles.

1. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

2. Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

3. High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning

4. Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

5. Machine learning for molecular and materials science

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