Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires
This work addresses the problem of improving computational efficiency and accuracy in material science simulations for researchers, but it is incremental as it builds on existing methods like Tersoff potentials and Gaussian processes.
The researchers tackled the trade-off between speed and accuracy in material modeling by developing empirical potentials for Si:H nanowires, using multi-fidelity Gaussian process regression to integrate low-fidelity empirical potential data with high-fidelity first-principle calculations, resulting in accurate predictions of H-H binding energy and H2-H2 interaction energy as demonstrated numerically.
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.