Computational Discovery and Design of Materials : Session II
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee
Program Organizers: Houlong Zhuang, Arizona State University; Duyu Chen, University of California, Santa Barbara; Ismaila Dabo, Pennsylvania State University; Yang Jiao, Arizona State University; Sara Kadkhodaei, University Of Illinois Chicago; Mahesh Neupane, Army Research Laboratory; Xiaofeng Qian, Texas A&M University; Arunima Singh, Arizona State University; Natasha Vermaak, Lehigh University
Monday 2:00 PM
March 20, 2023
Room: Cobalt 502A
Location: Hilton
Session Chair: Ismaila Dabo, Penn State University; Arunima Singh, Arizona State University
2:00 PM
Designing Ohmic and Schottky Interfaces for Oxide Electronics: Valentino Cooper1; Matthew Brahlek1; 1Oak Ridge National Laboratory
Band alignment and charge transfer at complex oxide interfaces is critical to tailoring their diverse functionality. Depending on the interface, Ohmic- or Schottky-like charge transfer interfaces emerge. Here, I present a method for predicting band alignment and charge transfer in ABO3 perovskites, thereby explaining recent experimental observations where previously established rules for simple semiconductors fail. I discuss a prototypical interface comprised of SrBO3 (metal; B=V, Nb and Ta), and semiconducting SrTiO3. For B=Nb and Ta significant charge transfer to SrTiO3 occurs due to higher energy Nb and Ta states relative to Ti. This induces a high mobility metallic interface, i.e., Ohmic contact. Conversely, for B=V there is no charge transfer to SrTiO3, i.e., Schottky contact. This work opens the door to integrating ABO3 perovskites into a range of practical devices. Supported by the U.S. D.O.E., Office of Science, BES, MSED using resources at NERSC and OLCF.
2:30 PM Invited
Searching for New "Quantum Defects" through High-throughput Computational Screening: Geoffroy Hautier1; 1Dartmouth College
Quantum Information Science (QIS) offers many opportunities in terms of computations and sensing. Point defects have emerged as powerful platforms for QIS with important "quantum defects" such as the NV center, or divacancy in diamond. Though these quantum defects have enabled the fundamental studies and engineering of some quantum technologies, they are not yet optimal for many applications. Thus, it is imperative to systematically investigate promising quantum defects that have optimal electronic and optical properties for a specific applications. I will report on our recent efforts to search for such an optimal quantum defect using high-throughput computing in two important material systems: silicon and 2D materials such as WS2.
3:00 PM
Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures: Cody Milne1; Arunima Singh1; Tathagata Biswas1; 1Arizona State University
Ultra-wide band gap (UWBG) materials offer a promising avenue for the future of power electronics. Devices made from UWBG materials can operate at much higher voltages, frequencies, and temperatures than current silicon-based devices. Aluminum nitride and boron nitride are UWBG semiconducting materials of great interest. However, the BxAl1-xN alloy material space has not been studied thoroughly. We predict the crystal structure of BxAl1-xN using density-functional theory simulations (DFT) and the cluster expansion method. We identify 19 ground state structures over the entire x=0 to x=1 composition range of BxAl1-xN. Excited state theory simulations under the GW approximation show that these ground states have very large band gaps. We report their effective masses and static dielectric constants, and perform structural analysis. Our results indicate that BxAl1-xN materials provide a wide range of tunable properties that promise increased performance in electronic devices.
3:20 PM
Crystal to PNG (xtal2png): A Screening Tool to Accelerate Domain Transfer from State-of-the-art Image-processing Models to Materials Informatics and a Case Study on Denoising Diffusion Probabilistic Models: Sterling Baird1; Kevin Jablonka2; Michael Alverson3; Hasan Sayeed1; Faris Khan1; Colton Seegmiller4; Berend Smit2; Taylor Sparks1; 1University of Utah; 2École Polytechnique Fédérale de Lausanne; 3University of Southern California; 4Utah Valley University
The latest advances in machine learning are often in natural language such as with long short-term memory networks (LSTMs) and transformers or image processing such as with generative adversarial networks (GANs), variational autoencoders (VAEs), and guided diffusion models. Using xtal2png to encode/decode crystal structures via grayscale PNG images is akin to making/reading a QR code for crystal structures. This allows you, as a materials informatics practitioner, to get streamlined results for new state-of-the-art image-based machine learning models applied to crystal structure. To showcase xtal2png, we apply it to several denoising diffusion probabilistic models (DDPMs), a recent state-of-the-art model in the image-processing domain. We also provide results using our recently developed generative benchmarking framework (https://matbench-generative.readthedocs.io/) and compare with CDVAE, a state-of-the-art generative model for crystal structures. The xtal2png Python package is open-source and documentation can be found at https://xtal2png.readthedocs.io/