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Molecular geometry optimization by artificial neural networks
Descriptive
TitleInfo
Title
Molecular geometry optimization by artificial neural
networks
networks
Name
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Chen
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He
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1990-
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He Chen
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author
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Elgammal
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Ahmed
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Ahmed Elgammal
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Advisory Committee
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chair
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Awasthi
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Pranjal
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Pranjal Awasthi
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internal member
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Steiger
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William
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William Steiger
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Advisory Committee
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Rutgers University
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School of Graduate Studies
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Text
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theses
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2019
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2019-01
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2019
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xx
Language
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eng
Abstract
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Articial neural network is revolutionizing many areas in science and technology. We applied articial neural network to solve a non-linear optimization problem in computational chemistry, i.e. molecular geometry optimization, which aims to nd an atomic arrangement that corresponds to a stationary point on the potential energy surface.
The implemented ANN can use both function values and derivatives as the reference data for training. The relative importance of function values and derivatives is studied extensively. With the same amount data points, ANN trained with derivatives tend to generalize better. With only derivatives as the reference data, the trained ANN can predict function values accurately if a common offset is allowed.
We trained ANNs that can predict molecule energies and gradients fairly well. Molecular geometry optimization is performed on testing data that are never seen during the training process. About 5% of the testing structures converge with average 0.047-angstrom RMSD compared with equilibrium state, while others diverge. The divergence is ascribed to poor gradient prediction near the equilibrium.
The implemented ANN can use both function values and derivatives as the reference data for training. The relative importance of function values and derivatives is studied extensively. With the same amount data points, ANN trained with derivatives tend to generalize better. With only derivatives as the reference data, the trained ANN can predict function values accurately if a common offset is allowed.
We trained ANNs that can predict molecule energies and gradients fairly well. Molecular geometry optimization is performed on testing data that are never seen during the training process. About 5% of the testing structures converge with average 0.047-angstrom RMSD compared with equilibrium state, while others diverge. The divergence is ascribed to poor gradient prediction near the equilibrium.
Subject
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Topic
Computer Science
Subject
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Topic
Neural networks (Computer science)
Subject
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Topic
Stereochemistry
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Rutgers University Electronic Theses and Dissertations
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ETD_9508
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electronic resource
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application/pdf
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text/xml
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1 online resource (57 pages : illustrations)
Note
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M.S.
Note
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Includes bibliographical references
Note
(type = statement of responsibility)
by He Chen
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/t3-y8vf-g439
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
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Chen
GivenName
He
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Type
Permission or license
DateTime
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2019-01-08 19:33:31
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Name
He Chen
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Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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License
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Embargo
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2019-01-31
DateTime
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2019-08-02
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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1.5
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2019-01-07T22:22:24
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2019-01-07T22:22:24
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