Materialism: A Materials Science Podcast

Episode 88: Accelerating Materials Discovery with Microsoft

May 8, 2024

Explore how Microsoft is revolutionizing material discovery through machine learning and AI, focusing on lithium-ion battery electrolytes. They use a filtering process to identify promising candidates and partner with PNNL for synthesis. Learn about their methodology and future opportunities in this exciting field.

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Introduction

00:00 • 2min

Advancements in Computational Materials Design

01:47 • 15min

Accelerating Material Discovery with Computational Methods and AI

17:14 • 7min

Exploring Data Applications and Material Filtering for Discovery

24:22 • 2min

Advancing Materials Discovery with Microsoft's Tools and National Resources

26:17 • 2min

Exploring and Synthesizing Promising Materials for Battery Compounds

27:55 • 2min

Accelerating Materials Discovery Through AI and Experimental Investigation

29:39 • 5min

Exciting Developments in Materials Science and Microsoft's Role in Accelerating Discovery

34:49 • 2min

The discovery of new materials is an immense challenge, with a vast design space and numerous success criteria. Microsoft has recently demonstrated an advanced approach to machine learning-assisted material discovery, particularly in the realm of lithium-ion battery electrolytes.

They began by exploring all possible structure types, decorating these structures with various atoms, leading to a pool of millions of candidate materials. The screening process went beyond simple stability checks to encompass a broad range of criteria, including predicted properties, electrode stability, and cost. This was achieved through various layers of filtering, leveraging data from diverse calculations, ranging from costly DFT and MD simulations to lower-fidelity calculations.

Microsoft wisely positioned the expensive calculations towards the end of the pipeline, focusing resources only on the most promising candidates. Furthermore, they partnered with the Pacific Northwest National Laboratory (PNNL) to synthesize the compounds identified.

In this podcast, we'll delve into this process, the challenges faced, and the future opportunities in this field, in conversation with Chi Chen and Nathan Baker.

If you want more details on teh work microsoft is doing in this space, you can check out their paper where they provide more details on the methodology and experimental results.

This episode of the Materialism Podcast is sponsored by Microsoft Azure Quantum Elements. You can try out their new copilot tools in an online demo on their Copilot Website. And if you want to learn more about how Microsoft is accelerating scientific discovery, you can head over to the Microsoft Azure Quantum Elements Website.

Thanks to Kolobyte and Alphabot for letting us use their music in the show!

If you have questions or feedback please send us emails at materialism.podcast@gmail.com or connect with us on social media: Instagram, Twitter.

Materialism Team: Taylor Sparks (co-host, co-creator), Andrew Falkowski (co-host, co-creator), Jared Duffy (production, marketing, and editing).

Keywords: AI Quantum Microsoft Materials Acceleration Battery Lithium Ion Li