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

开发具有低导热性、超低导热性或各向异性导热性的新材料可能会实现新型能量存储和转换设备，以及电子产品的有效热管理。晶界，通常存在于固体材料中，显着影响热传输，因此工程晶界是至关重要的，在开发所需的热性能的新材料。由于获得实验数据的挑战和依赖于采用经验势的模拟研究的局限性，跨晶界的热输运还没有得到很好的理解。拟议的研究将开发一个新的多尺度仿真框架，结合机器学习技术和第一性原理计算。新框架具有与直接第一性原理计算相当的高精度，并且在计算上是可行的，从而能够发现跨晶界的热传输过程的基本物理。还提出了一些教育活动，以提高公众的认识，特别是关于机器学习技术如何改变基础科学和工程研究。这个CAREER项目的目标是建立一个定量的理解，通过各种类型的晶界与第一原理的高预测能力的热输运。新的多尺度模拟框架有可能保持计算成本几个数量级的便宜比直接的第一性原理计算。这是通过集成（i）机器学习的局部原子势景观原子间势在~ 1纳米尺度，（ii）原子绿色的函数方法的声子散射晶界在10至100纳米尺度，和（ii）的Peierls-Boltzmann输运理论的整体声子输运在亚毫米尺度。利用这个新的模拟框架，该项目将寻求获得声子输运在几个实际相关的2D和3D半导体多晶体的结论性理解。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.