First-Principles Prediction of Electronic Transport in Experimental Semiconductor Heterostructures via Physics-Based Machine Learning

Abstract

First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to model heterostructures incorporating variability due to fabrication processes. Machine-learning (ML)-based materials informatics approaches (MI) are increasingly used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited MI to learn electronic structure properties and use the knowledge to predict the respective transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of silicon/germanium heterostructures beyond the length-scale accessible with first-principles techniques, matching measured data. We demonstrate application of MI to extract important physics that determines electronic transport in semiconductor heterostructures, breaking from combinatorial strategies pursued especially for thermoelectric materials. We anticipate that ETI would have broad applicability to diverse materials classes.

arXiv comment14 pages, 7 figures

References in corpus (15)

• BoltzTraP. A code for calculating band-structure dependent quantities
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Big Data of Materials Science - Critical Role of the Descriptor
• Finding Density Functionals with Machine Learning
• Understanding Band Gaps of Solids in Generalized Kohn-Sham Theory
• A recommendation engine for suggesting unexpected thermoelectric chemistries
• Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
• Tunable singlet-triplet splitting in a few-electron Si/SiGe quantum dot
• Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
• Predicting charge density distribution of materials using a local-environment-based graph convolutional network
• Hierarchical nanostructuring approaches for thermoelectric materials with high power factors
• Thermoelectric transport in strained Si and Si/Ge heterostructures
• Heat and charge transport in bulk semiconductors with interstitial defects
• Theoretical Prediction of Enhanced Thermopower in n-doped Si/Ge Superlattices using Effective Mass Approximation

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• Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations of Periodic Nanostructures
• Modulation of Semiconductor Superlattice Thermopower Through Symmetry and Strain