Nancy Black is inviting you to a scheduled Zoom meeting. Join Zoom Meeting https://purdue-edu.zoom.us/j/98695180597 Meeting ID: 986 9518 0597 One tap mobile +16465588656,,98695180597# US (New York) 13017158592,,98695180597# US +(Washington DC) Dial by your location +1 646 558 8656 US (New York) +1 301 715 8592 US (Washington DC) +1 312 626 6799 US (Chicago) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) +1 346 248 7799 US (Houston) Meeting ID: 986 9518 0597 Find your local number: https://purdue-edu.zoom.us/u/abtkh4zmcl Arun Kumar Mannodi Kanakkithodi Assistant Professor, School of Materials Engineering, Purdue University September 30th, 2021 | 12:00pm Join Zoom: https://purdue-edu.zoom.us/j/98695180597 [Arun Kumar Mannodi Kanakkithodi]Title: Tuning Optoelectronic Properties of Semiconductors using High-Throughput Computations and Machine Learning Abstract: Semiconductors with desirable electronic band structure and optical absorption are sought for solar cells, electronic devices, infrared sensors, and quantum computing. Compositional manipulation via alloying at cation or anion sites, or via incorporation of point defects and impurities, can help tune the properties of semiconductors in known chemical spaces. In this work, we develop AI-based frameworks for the on-demand prediction of the phase stability, band gap, optical absorption spectra, photovoltaic figures of merit, defect formation energies, and impurity energy levels in two broad classes of semiconductors, namely (a) halide perovskites with the general formula ABX3 (where A is a large organic or inorganic monovalent cation, B is a divalent cation and X is a halogen anion), and (b) group IV, III-V and II-VI semiconductors in the zinc blende structure. These frameworks are powered by high-throughput density functional theory (DFT) computations, unique encoding of the atom-composition-structure information, and rigorous training of advanced neural network-based predictive and optimization models. Multi-fidelity learning is applied to bridge the gap between (high quantities of) low accuracy calculations and (lower quantities of) high-fidelity data, constituted of data from advanced DFT functionals. AI-based recommendations are synergistically coupled with targeted synthesis and characterization, leading to successful validation and discovery of novel compositions for improved performance in solar cells. Bio: Arun Mannodi Kanakkithodi is an assistant professor in Materials Engineering at Purdue university. He received his PhD in Materials Science and Engineering from the University of Connecticut in 2017 and worked as a postdoctoral researcher at the Center for Nanoscale Materials in Argonne National Laboratory from 2017 to 2020. His research involves using first principles computational modeling, machine learning, and materials informatics to drive the design of new materials for energy-relevant applications. He is a contributor to the NSF-funded nanoHUB.org and a co-organizer of the hands-on data science and machine learning workshop series: https://nanohub.org/groups/ml/handsontraining. Previously recorded talks: https://engineering.purdue.edu/Intranet/Groups/BNC/FacultySeminars Upcoming BNC Virtual Faculty Seminars and Recorded Talks, Fall 2021: Date Faculty Title 10/7/21 Michelle Thompson, Assistant Professor, Department of Earth, Atmospheric, and Planetary Sciences
From Atomic Scales to Asteroid Surfaces: Understanding Airless Bodies through Coordinated Analyses 10/14/21 Xiaoping Bao, Assistant Professor, Davidson School of Chemical Engineering Engineer and Manufacture Off-the-Shelf CAR-NK Cells for Targeted Cancer Immunotherapy 10/21/21 10/28/21 Andres Arrieta, Assistant Professor, School of Mechanical Engineering
11/4/21 Tian Li, Assistant Professor of Mechanical Engineering Naturally Nanostructured Cellulose towards Energy Water Nexus 11/11/21 Caitlin Proctor, Assistant Professor of Agricultural and Biological Engineering & Environmental and Ecological Engineering Biofilms in Everyday Life 11/18/21
