Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons-Reference-Cited by-同舟云学术

Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons

Published:2022-02-17 Issue:3 Volume:2 Page:260-264
ISSN:2694-2445
Container-title:ACS Physical Chemistry Au
language:en
Short-container-title:ACS Phys. Chem Au

Author:

Ha Miran¹^ORCID,Hajibabaei Amir¹^ORCID,Pourasad Saeed¹,Kim Kwang S.¹^ORCID

Affiliation:

1. Center for Superfunctional Materials, Department of Chemistry, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea

Funder

Ulsan National Institute of Science and Technology

National Research Foundation of Korea

Korea Institute of Science and Technology Information

Publisher

American Chemical Society (ACS)

Subject

General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acsphyschemau.1c00058

Reference42 articles.

1. Optimized intermolecular potential functions for liquid hydrocarbons

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4. CHARMM: The biomolecular simulation program

5. A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules

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