Gaussian Process Regression for Absorption Spectra Analysis of Molecular Dimers

• URL: http://arxiv.org/abs/2112.07590v2
• Date: Wed, 15 Dec 2021 08:36:44 GMT
• Title: Gaussian Process Regression for Absorption Spectra Analysis of Molecular Dimers
• Authors: Farhad Taher-Ghahramani and Fulu Zheng and Alexander Eisfeld
• Abstract summary: We discuss an approach based on a machine learning technique, where the parameters for the numerical calculations are chosen from Gaussian Process Regression (GPR) This approach does not only quickly converge to an optimal parameter set, but in addition provides information about the complete parameter space. We find that indeed the GPR gives reliable results which are in agreement with direct calculations of these parameters using quantum chemical methods.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A common task is the determination of system parameters from spectroscopy, where one compares the experimental spectrum with calculated spectra, that depend on the desired parameters. Here we discuss an approach based on a machine learning technique, where the parameters for the numerical calculations are chosen from Gaussian Process Regression (GPR). This approach does not only quickly converge to an optimal parameter set, but in addition provides information about the complete parameter space, which allows for example to identify extended parameter regions where numerical spectra are consistent with the experimental one. We consider as example dimers of organic molecules and aim at extracting in particular the interaction between the monomers, and their mutual orientation. We find that indeed the GPR gives reliable results which are in agreement with direct calculations of these parameters using quantum chemical methods.

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