AI for Materials Discovery with Greg Mulholland

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode I’m joined by Greg Mulholland, Founder and CEO of Citrine Informatics, which is applying AI to the discovery and development of new materials.

Greg and I start out with an exploration of some of the challenges of the status quo in materials science, and what’s to be gained by introducing machine learning into this process. We discuss how limitations in materials manifest themselves, and Greg shares a few examples from the company’s work optimizing battery components and solar cells. We dig into the role and sources of data used in applying ML in materials, and some of the unique challenges to collecting it, and discuss the pipeline and algorithms Citrine uses to deliver its service. This was a fun conversation that spans physics, chemistry, and of course machine learning, and I hope you enjoy it.

Interested in the Fast.AI Deep Learning course?

This Saturday we discuss lesson one, “Recognizing Cats and Dogs” of the Fast AI Practical Deep Learning for Coders course via the Meetup. This is a great course and co-founder Jeremy Howard encouraged our group on twitter noting that groups that take the course together have a higher success rate, so let’s do this!

Three simple steps to join:
1. Sign up for the Meetup, noting in the “What you hope to learn” box
2. Using the email invitation you’ll receive to join our Slack group, and
3. Once you’re there joining the #fast_ai channel.

About Greg

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

  • Ben

    I love your podcast, but sometimes you need to push harder. When you ask for specifics about what they do, what algorithms they run, etc., and they reply with five minutes about how they feed data in and get results back, and then feed more data in and get more results, and how this is the scientific method…, you should either interrupt or ask the same question again. After listening to this podcast, I still have no idea what they do or how. And I was really interested.

    • sam

      Fair enough. Thanks for the feedback. In this case, I made the call that the use case context was interesting enough to share, but there have certainly been cases where I couldn’t drive to enough detail to make the interview worth publishing and just don’t.

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