Nakhmanson, Serge | Search NASA

DOE OSTI2024

Site-Projected Thermal Conductivity: Application to Defects, Interfaces, and Homogeneously Disordered Materials

With the rapid advance of high-performance computing and electronic technologies, understanding thermal conductivity in materials has become increasingly important. This study presents a novel method: the site-projected thermal conductivity that quantitatively estimates the local (atomic) contribution to heat transport, leveraging the Green–Kubo thermal transport equations. The effectiveness of this approach on disordered and amorphous graphene, amorphous silicon, and grain boundaries in silicon–germanium alloys is demonstrated. Amorphous graphene reveals a percolation behavior for thermal transport. The results highlight the potential of the method to provide new insights into the thermal behavior of materials, offering a promising avenue for materials design and performance optimization.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Identification of novel organic polar materials: A machine learning study with importance sampling

Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporation into modern electronic devices. However, in order to realize the full potential of polar polymer and molecular crystals for modern technological applications, it is paramount to assemble and evaluate all the available data for such compounds, identifying descriptors that could be associated with an emergence of ferroelectricity. In this paper, we utilized data-driven approaches to judiciously shortlist candidate materials from a wide chemical space that could possess ferroelectric functionalities. A machine learning study with importance sampling was employed to address the challenge of having a limited amount of available data on already-known organic ferroelectrics. Sets of molecular- and crystal-level descriptors were combined with a Random Forest Regression algorithm in order to predict the spontaneous polarization of the shortlisted compounds. First-principles simulations were performed to further validate the predictions obtained from the machine learning model.

36 MATERIALS SCIENCE↗