Journal article
Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network
Journal of chemical information and modeling, Vol.60(4), pp.1928-1935
04/27/2020
DOI: 10.1021/acs.jcim.9b00835
PMID: 32053367
Abstract
The use of machine learning in chemistry is on the rise for the prediction of chemical properties. The input feature representation or descriptor in these applications is an important factor that affects the accuracy as well as the extent of the explored chemical space. Here, we present the periodic table tensor descriptor that combines features from Behler-Parrinello's symmetry functions and a periodic table representation. Using our descriptor and a convolutional neural network model, we achieved 2.2 kcal/mol and 94 meV/atom mean absolute error for the prediction of the atomization energy of organic molecules in the QM9 data set and the formation energy of materials from Materials Project data set, respectively. We also show that structures optimized with a force field derived from this model can be used as input to predict the atomization energies of molecules at density functional theory level. Our approach extends the application of Behler-Parrinello's symmetry functions without a limitation on the number of elements, which is highly promising for universal property calculators in large chemical spaces.
Details
- Title: Subtitle
- Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network
- Creators
- Balaranjan Selvaratnam - University of South DakotaRanjit T. Koodali - University of South DakotaPere Miro - University of South Dakota
- Resource Type
- Journal article
- Publication Details
- Journal of chemical information and modeling, Vol.60(4), pp.1928-1935
- Publisher
- Amer Chemical Soc
- DOI
- 10.1021/acs.jcim.9b00835
- PMID
- 32053367
- ISSN
- 1549-9596
- eISSN
- 1549-960X
- Number of pages
- 8
- Grant note
- OAC-1626516 / NSF; National Science Foundation (NSF) Department of Chemistry of the University of South Dakota (USD) DGE1633213 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 04/27/2020
- Academic Unit
- Chemistry
- Record Identifier
- 9984618504702771
Metrics
15 Record Views