Topological descriptor of thermal conductivity in amorphous materials

Abstract

Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a long-standing problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remain elusive. Here, we solve this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. By using persistent homology, we constructed a topological descriptor that can predict the thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features that correlated with both the thermal conductivity and MRO. The results provide an avenue for controlling the material characteristics through the topology of nanostructures.

arXiv comment27 pages, 10 figures

References in corpus (5)

• A roadmap for the computation of persistent homology
• Hierarchical structures of amorphous solids characterized by persistent homology
• Propagating elastic vibrations dominate thermal conduction in amorphous silicon
• Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
• Large and realistic models of Amorphous Silicon

Cited by in corpus (4)

• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Ring-originated anisotropy of local structural ordering in amorphous and crystalline silicon dioxide
• Topological Data Analysis of Ion Migration Mechanism
• Spatially Local Estimates of the Thermal Conductivity of Materials