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Season 4, Episode 4 -- Anubhav Jain: Hacking Materials

Season 4, Episode 4 -- Anubhav Jain: Hacking Materials

FromScience in Parallel


Season 4, Episode 4 -- Anubhav Jain: Hacking Materials

FromScience in Parallel

ratings:
Length:
33 minutes
Released:
Nov 8, 2023
Format:
Podcast episode

Description

Artificial intelligence is reshaping research to discover new materials for a range of important applications. In this episode, meet Anubhav Jain of Lawrence Berkeley National Laboratory, a researcher who has been at the forefront of this transition. He uses machine learning and other computational tools as a materials scientist to discover compounds that could store and convert energy and solve other societal problems. Anubhav’s current research path started in graduate school at MIT, where he was supported by a Department of Energy Computational Science Graduate Fellowship. We discuss how computational tools including AI have moved from a novel idea to a central piece of materials discovery, how he applies machine learning tools to other tasks such as mining data from scientific papers, and the rewards that came from writing his blog called Hacking Materials. This episode concludes our season 4 series on creativity in computing.
Released:
Nov 8, 2023
Format:
Podcast episode

Titles in the series (22)

Computers and science are intertwined – and not just as tools that help humans connect and collaborate. With computers, scientists model the earth’s climate, design alternative energy strategies and simulate exploding stars. From laptops to the world’s fastest supercomputers, software innovations and artificial intelligence are reshaping how we interact with mounds of data from healthcare to high-energy physics and how we solve critical problems. Computational science brings together mathematics, computer science and hardware and science expertise to take on these challenges. In this podcast, you’ll meet the scientists doing this work, learn more about their research and gain insights into the workings of this dynamic field.