🔬Searching the Space of All Possible Materials — Prof. Max Welling, CuspAI
February 25, 2026
AI Summary
5 min read🎙️ The Voices & The Context
- The Format: An in-depth interview podcast with a host engaging a renowned AI researcher on his career, physics-inspired ML innovations, and his climate-focused startup.
- The Key Players:
- Guest: Max Welling – Pioneering AI scientist famous for variational autoencoders (VAEs), graph neural networks, and equivariance in ML; founded Caspei, an AI platform for materials discovery to combat climate change. His physics background (quantum gravity PhD) ties everything together.
- Host: Enthusiastic interviewer, probing Max's ideas with follow-ups on AI for science and practical applications; great rapport, blending technical depth with accessible questions.
- The Vibe: Educational yet exciting – Intellectual thrill of big ideas (physics + AI), optimistic about world-changing impact, with a touch of humility on tech limits.
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What you'll learn
- 1 (00:00) **🎙️ Introduction: Max Welling**
- 2 (01:16) **Career Thread and Excitement Drivers**
- 3 (03:20) **Physics as Unifying Thread in ML**
- 4 (07:19) **Rise of AI for Science**
- 5 (09:43) **How AI Engineers Enter AI for Science**
- 6 (11:20) **Materials as Foundation for Impact**
- 7 (14:24) **Caspe AI Overview and Vision**
+ Full timestamped outline available in the app
Show Notes
Editor’s note: CuspAI raised a $100m Series A in September and is rumored to have reached a unicorn valuation. They have all-star advisors from Geoff Hinton to Yann Lecun and team of deep domain experts to tackle this next frontier in AI applications.
In this episode, Max Welling traces the thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery (yes, there is one!!!).
We begin with a provocative framing: experiments as computation. Welling describes the idea of a “physics processing unit”—a world in which digital models and physical experiments work together, with nature itself acting as a kind of processor. It’s a grounded but ambitious vision of AI for science: not replacing chemists, but accelerating them.Along the way, we discuss:
* Why symmetry and equivariance matter in deep learning
* The tradeoff between scale and inductive bias
* The deep mathematical links between diffusion models and stochastic thermodynamics
* Why materials—not software—may be the real bottleneck for AI and the energy transition
* What it actually takes to build an AI-driven materials platform
Max reflects on moving from curiosity-driven theoretical physics (including work with Gerard ‘t Hooft) toward impact-driven research in climate and energy. The result is a conversation about convergence: physics and machine learning, digital models and laboratory experiments, long-term ambition and incremental progress.
Full Video Episode
Timestamps
* 00:00:00 – The Physics Processing Unit (PPU): Nature as the Ultimate Computer
* Max introduces the idea of a Physics Processing Unit — using real-world experiments as computation.
* 00:00:44 – From Quantum Gravity to AI for Materials
* Brandon frames Max’s career arc: VAE pioneer → equivariant GNNs → materials startup founder.
* 00:01:34 – Curiosity vs Impact: How His Motivation Evolved
* Max explains the shift from pure theoretical curiosity to climate-driven impact.
* 00:02:43 – Why CaspAI Exists: Technology as Climate S
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