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| Title: | An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters |
| Authors: | Jindal, Shweta Bulusu, Satya Silendra |
| Keywords: | Gold Nanoclusters;Molecular dynamics;Molecular physics;Nanoclusters;Potential energy;Potential energy surfaces;Power spectrum;Quantum chemistry;Thermodynamic properties;Bispectrum;Descriptors;Different frequency;Higher-order;Learning techniques;Molecular dynamics simulations;Order parameter;Phase information;Neural networks |
| Issue Date: | 2018 |
| Publisher: | Elsevier B.V. |
| Citation: | Jindal, S., & Bulusu, S. S. (2018). An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters. Chemical Physics Letters, 693, 152-158. doi:10.1016/j.cplett.2018.01.023 |
| Abstract: | In order to fit potential energy surface (PES) of gold nanoclusters, we have integrated bispectrum features with artificial neural network (ANN) learning technique in this work. We have also devised an algorithm for selecting the frequencies that need to be coupled for extracting the phase information between different frequency bands. We have found that higher order invariant like bispectrum is highly efficient in exploring the PES as compared to other invariants. The sensitivity of bispectrum can also be exploited in acting as an order parameter for calculating many thermodynamic properties of nanoclusters. © 2018 |
| URI: | https://doi.org/10.1016/j.cplett.2018.01.023 https://dspace.iiti.ac.in/handle/123456789/9051 |
| ISSN: | 0009-2614 |
| Type of Material: | Journal Article |
| Appears in Collections: | Department of Chemistry |
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