DFT aided machine learning interatomic potentials for realistic simulations of low dimensional system

Challenges in atomistic simulations of materials include limitations in time and length scales and lack of accurate interatomic forcefields. In this talk, we will discuss the application of machine learning to develop accurate interatomic forcefields for reducing computational cost and accessing time and length-scale relevant to experiments in atomistic simulations of materials through two examples: the semiconducting-metallic transition of the Si(100) surface, and heat transport through grain boundaries in hexagonal boron nitride. Artificial Neural network (ANN) interatomic potentials are trained using large databases of structure-energy relationship of small representative systems obtained from ab initio molecular dynamic simulations, based on density functional theory (DFT). The trained ANN potentials produce potential energies that are in good agreement with those from DFT. For Si(001), results from our molecular dynamics (MD) simulations using ANN potential show that the asymmetric, buckled structure of Si dimer exists in the temperature range 300K to 900K, but the increased dimer flipping rate leads to more time in the symmetric configuration making the surface metallic. Moreover, our simulations indicate persistence of Si adatoms, formed by the breakage of Si-dimers, above 800K, that can move around on the surface, which explain the jump in metallicity of the surface around this temperature, as seen in experiments [1]. For h-BN, non-equilibrium MD simulations using ANN potential are performed to study thermal conductivity of h-BN. We show that thermal properties calculated by our simulations for pristine h-BN agree well with experimental data [2] and that grain-boundaries in h-BN hinders heat transport through h-BN, increasing its thermal resistant.

[1] C. Jeon et al, Phys. Rev. B 80, 153306 (2009).

[2] C. Yuan et al., Communications Physics 2, 43 (2019)