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Machine Learned Force Fields

Title: Machine Learned Force Fields.
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Name(s): Sheridan, Cole Nathaniel, author
Type of Resource: text
Genre: Text
Bachelor Thesis
Date Issued: 2020-11-20
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In this paper, we perform a detailed review of replication of traditional ab inito molecular dynamics methods to generate molecular force fields utilizing artificial neural networks (ANNs). This is acomplished through the representation of diatomic C-X system in one dimension, with an analysis of the overfitting problem of ANNs, and applying ANNs to the study of a cyanopolyyne molecule.
Identifier: FSU_libsubv1_scholarship_submission_1606147090_89a2a7f7 (IID)
Keywords: Machine Learning, ANN, Artificial Nerual Network, Molecular Dynamics
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1606147090_89a2a7f7
Use and Reproduction: Creative Commons Attribution-ShareAlike (CC BY-SA 4.0)
Host Institution: FSU

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Sheridan, C. N. (2020). Machine Learned Force Fields. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1606147090_89a2a7f7