Speaker
Description
One of the fundamental properties of semiconductors is their ability to support electric currents in the presence of electric and magnetic fields. These properties are described by transport coefficients such as drift and Hall electron and hole mobilities. During the past decade, there has been considerable progress in first-principles atomic-scale calculations of these coefficients by combining density functional theory, many-body perturbation theory, and the Boltzmann transport equation. The reliability, accuracy, and reproducibility of these calculations keep improving at a fast pace, and we are at a point where state-of-the-art methods and software are ready for data-driven approaches and machine learning tasks. In this talk, I will describe the Boltzmann transport solver of the software package EPW, and I will report on recent work on the high-throughput search for high-mobility n-type and p-type 2D channel materials for nanoscale transistors, and our first attempts at incorporating machine learning tools such as random forest regression. In addition, I will discuss recent work in our group on the development of streamlined workflows for electron-phonon physics, namely the MATCSSI cloud integration and the EPWpy abstraction and automation library. MATCSSI is a joint effort between the University of Texas, the University of California at Berkeley, the University of Binghamton, and the Texas Advanced Computing Center (TACC) aimed at making advanced many-body electronic structure calculations more accessible and more streamlined. This cloud portal supports Jupyter Notebooks which are executed directly on TACC supercomputers through a custom-made JupyterHPC integration. The aim of this initiative is to lower the barrier to entry for new users interested in advanced electronic structure calculations, including fully first-principles calculations of transport coefficients. EPWpy is a Python library that allows users to define materials as objects, and supports a high-level syntax to specify ab initio calculation workflows as methods on such objects. EPWpy aims for a lightweight, user-friendly experience that is intuitive for both users and developers, enables automation for machine learning tasks, and can be used as a standalone platform or in combination with the MATCSSI cloud portal.
