Gaussian Process Regression Adaptive Density-Guided Approach: Towards Calculations of Potential Energy Surfaces for Larger Molecules

TL;DR

This paper introduces an improved Gaussian process regression approach for calculating potential energy surfaces of larger molecules, significantly reducing computational costs while maintaining high accuracy for vibrational spectra simulations.

Contribution

The authors extend the GPR-ADGA method to larger molecular systems with technical improvements, enabling high-accuracy potential energy surface calculations more efficiently.

Findings

Up to 80% of single point calculations can be avoided.

Achieved RMS deviation of about 3 cm$^{-1}$ in fundamental excitations.

Errors below 1 cm$^{-1}$ with tighter convergence thresholds, reducing computations by 68%.

Abstract

We present a new program implementation of the gaussian process regression adaptive density-guided approach [J. Chem. Phys. 153 (2020) 064105] in the MidasCpp program. A number of technical and methodological improvements made allowed us to extend this approach towards calculations of larger molecular systems than those accessible previously and maintain the very high accuracy of constructed potential energy surfaces. We demonstrate the performance of this method on a test set of molecules of growing size and show that up to 80 % of single point calculations could be avoided introducing a root mean square deviation in fundamental excitations of about 3 cm$^{-1}$. A much higher accuracy with errors below 1 cm$^{-1}$ could be achieved with tighter convergence thresholds still reducing the number of single point computations by up to 68 %. We further support our findings with a detailed analysis of wall times measured while employing different electronic structure methods. Our results demonstrate that GPR-ADGA is an effective tool, which could be applied for cost-efficient calculations of potential energy surfaces suitable for highly-accurate vibrational spectra simulations.