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 [email protected] 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