Accelerating Coupled Cluster Calculations with Nonlinear Dynamics and Shallow Machine Learning
| Autor: | Samrendra Roy, Anish Chakraborty, Rahul Maitra, Valay Agarawal |
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| Rok vydání: | 2020 |
| Předmět: |
General Physics and Astronomy
FOS: Physical sciences 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Reduction (complexity) Set (abstract data type) Condensed Matter - Strongly Correlated Electrons Polynomial kernel 0103 physical sciences Cluster (physics) Physical and Theoretical Chemistry Mathematics 010304 chemical physics Strongly Correlated Electrons (cond-mat.str-el) business.industry Computational Physics (physics.comp-ph) 0104 chemical sciences Nonlinear system Coupled cluster Amplitude Phase space Artificial intelligence business computer Physics - Computational Physics |
| DOI: | 10.48550/arxiv.2011.02259 |
| Popis: | The dynamics associated with the time series of the iteration scheme of coupled cluster theory has been analysed. The phase space analysis indicates the presence of a few significant cluster amplitudes, mostly involving valence excitations, which dictate the dynamics, while all other amplitudes are enslaved. Starting with a few initial iterations to establish the inter-relationship among the cluster amplitudes, a supervised Machine Learning scheme with polynomial Kernel Ridge Regression model has been employed to express each of the enslaved variables uniquely in terms of the master amplitudes. The subsequent coupled cluster iterations are restricted to a reduced dimension only to determine those significant excitations, and the enslaved variables are determined through the already established functional mapping. We will show that our scheme leads to tremendous reduction in computational time without sacrificing the accuracy. Comment: 8 pages, 8 figures (including 1 Tikz Cartoon) |
| Databáze: | OpenAIRE |
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