CAREER: Machine Learning Enabled Study of Thermal Transport in Polycrystalline Materials from First Principles

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The development of new materials with ultrahigh, ultralow, or anisotropic thermal conductivity can potentially enable novel energy storage and conversion devices, and effective thermal management of electronics. Grain boundaries, which commonly exist in solid materials, significantly affect thermal transport, and thus engineering grain boundaries is crucial in developing novel materials of desired thermal properties. Due to challenges in obtaining experimental data and limitations in simulation studies that rely on employing empirical potentials, thermal transport across grain boundaries is not well understood. The proposed research will develop a new multiscale simulation framework that combines machine learning techniques and first-principles calculations. The new framework has a high accuracy comparable to direct first-principles calculations and is computationally feasible, enabling the discovery of the underlying physics of thermal transport processes across grain boundaries. Several educational activities are also proposed to increase public awareness, particularly about how machine learning techniques transform the basic science and engineering research. The goal of this CAREER project is to establish a quantitative understanding of thermal transport across various types of grain boundaries with the high predictive power of first principles. The new multiscale simulation framework has the potential to keep the computational cost several orders-of-magnitude cheaper than the direct first-principles calculation. This is made possible by integrating (i) machine learning of interatomic potentials for local atomic potential landscape at ~ 1 nm scale, (ii) atomistic Green's function method for phonon scattering by grain boundaries at 10 to 100 nm scale, and (ii) the Peierls-Boltzmann transport theory for overall phonon transport at sub-mm scale. Using this new simulation framework, this project will seek to obtain a conclusive understanding of phonon transport in several practically relevant 2D and 3D semiconductor polycrystals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.