Machine Learning-Driven Discovery of Solid State Electrolytes with Dr. Shreyas Honrao
Two major issues affecting Li-ion battery performance today are energy density and safety. All-solid-state batteries with a Li metal anode can address both of these issues — but the development of solid electrolytes that simultaneously possess high ionic conductivity and good chemical and electrochemical stability has proven to be a challenge.
To discuss this challenge, we welcome Aionics Senior Materials Informatics Scientist Dr. Shreyas Honrao to Aionics Fortnightly Episode 31. Dr. Honrao, who was previously a NASA scientist before joining Aionics, will discuss his work using tree-based machine learning methods for solid-state electrolyte discovery.
Furthermore, Dr. Honrao will explore how tree-based ensemble learning methods can be used to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus.
This is sure to be an interesting discussion — don’t miss out!
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Dr. Shreyas Honrao
Senior Materials Informatics Scientist, Aionics, Inc.
Dr. Shreyas Honrao is a materials researcher and data scientist at Aionics, Inc. working on new materials discovery and design. He holds a Ph.D. in Materials Science & Engineering from Cornell University, and four years of past experience as a researcher in the Computational Materials Group at NASA Ames. His research focuses on using computational modeling and machine learning techniques to study structure-property-processing relationships in materials. His main areas of interest include materials informatics, materials design, solid-state batteries, shape memory alloys, solid defects and diffusion.