Past Fortnightly
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!
Newsletter Subscription
Subscribe to our newsletter and stay updated with the latest in material informatics.
Guest bio
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.
