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Machine Learned Force Fields
| Title: | Machine Learned Force Fields. |
 7 views 0 downloads |
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| Name(s): | Sheridan, Cole Nathaniel, author | |
| Type of Resource: | text | |
| Genre: |
Text Bachelor Thesis |
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| 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 |
Sheridan, C. N. (2020). Machine Learned Force Fields. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_scholarship_submission_1606147090_89a2a7f7
