W2D2S2 - Shining Light on Perovskites

The Western Washington Data-driven Discovery Seminar Series (W2D2S2) is hosting a series of events in partnership with Pacific Northwest National Laboratory again this Fall on Thursdays from 3:00-4:00pm! Please keep an eye out and email w2d2s2organizers@gmail.com for the zoom invite link!

Keynote Speaker: Professor Rob Berger, Western Washington University

Title: Shining Light on Perovskites

Abstract: Compounds crystallizing in the ABX3 perovskite structure are studied for many applications, including solar energy conversion. This class of materials includes both lead-halide perovskites that absorb light in photovoltaic solar cells (e.g., CH3NH3PbI3) and transition metal-oxide perovskite photocatalysts (e.g., SrTiO3). One reason for the technological versatility of perovskites is that their composition and structure, and consequently their electronic properties, are highly tunable. These compounds can be engineered through elemental substitution, strain, layering, and defects, allowing for the optimization of their properties. I will summarize our group’s work over the past several years, in which we use density functional theory (DFT) calculations to explore how changes in composition and atomic structure affect the electronic structure and properties of perovskites for solar energy conversion. This work has led us to both fundamental understanding and concrete predictions of new materials.

Bio: Rob Berger is an associate professor in the Western Washington University Department of Chemistry. Since joining WWU in 2013, Dr. Berger’s research group has used computation to understand relationships among the atomic and electronic structure of solids for energy applications. In the classroom, Dr. Berger teaches primarily physical chemistry and general chemistry.

Keynote Speaker: Dr. Ankit Roy, Pacific Northwest National Laboratory

Title: Machine-Learning-Guided Descriptor Selection for Predicting Corrosion Resistance in Multi-Principal Element Alloys

Abstract: More than $270 billion is spent on combating corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict the corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (Δa) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like the corrosion of alloys.

Bio: Dr. Ankit Roy graduated from his PhD program in Mechanical Engineering from Lehigh University (PA) in August, 2021 and has been working for PNNL since Sept 2021. He works on Li-Al-O ceramics to model the radiation damage in them using molecular dynamics (MD). He is also working on modeling radiation damage in some Ti alloys using molecular dynamics. In his PhD program, he used DFT, MD and machine learning to explore the properties of high entropy alloys (HEAs).

See our W2D2S2 website for more information

Please email w2d2s2organizers@gmail.com for the zoom invite link!

Best,

The Fall 2022 W2D2S2 Organizing Committee

Sarah Akers, Ying Bao, Stefan Dernbach, Brian Hutchinson, Tim Kowalczyk, Kimihiro Noguchi, Mohammad Taufique