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Predicting polarizabilities of silicon clusters using local chemical environments
- Publication Year :
- 2021
- Publisher :
- Apollo - University of Cambridge Repository, 2021.
-
Abstract
- Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies.
- Subjects :
- Paper
Silicon
chemistry.chemical_element
FOS: Physical sciences
02 engineering and technology
01 natural sciences
Cross-validation
machine learning polarizabilities
Artificial Intelligence
0103 physical sciences
Cluster (physics)
Physics::Atomic and Molecular Clusters
Limit (mathematics)
Statistical physics
Physics::Atomic Physics
010306 general physics
Scaling
Physics
Condensed Matter - Materials Science
Silicon clusters
RPA polarizabilities of silicon clusters
Materials Science (cond-mat.mtrl-sci)
predicting polarizabilities of nanoparticles
021001 nanoscience & nanotechnology
Human-Computer Interaction
chemistry
Cluster size
silicon cluster polarizabilities
0210 nano-technology
Software
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....244ddc598ac1b97005e9305d212dec69
- Full Text :
- https://doi.org/10.17863/cam.77205