Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires

TL;DR

This paper develops a multi-fidelity Gaussian process approach to create more accurate empirical potentials for Si:H nanowires by integrating fast empirical calculations with precise first-principle data.

Contribution

It introduces a novel multi-fidelity Gaussian process method to improve empirical potential accuracy using combined low- and high-fidelity data.

Findings

Enhanced accuracy of empirical potentials for Si:H nanowires

Successful integration of multi-fidelity data improves predictions

Numerical results confirm the effectiveness of the developed potentials

Abstract

In material modeling, the calculation speed using the empirical potentials is fast compared to the first principle calculations, but the results are not as accurate as of the first principle calculations. First principle calculations are accurate but slow and very expensive to calculate. In this work, first, the H-H binding energy and H$_2$-H$_2$ interaction energy are calculated using the first principle calculations which can be applied to the Tersoff empirical potential. Second, the H-H parameters are estimated. After fitting H-H parameters, the mechanical properties are obtained. Finally, to integrate both the low-fidelity empirical potential data and the data from the high-fidelity first-principle calculations, the multi-fidelity Gaussian process regression is employed to predict the H-H binding energy and the H$_2$-H$_2$ interaction energy. Numerical results demonstrate the accuracy of the developed empirical potentials.