🔬Searching the Space of All Possible Materials — Prof. Max Welling, CuspAI

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 Strategy

* Politics struggles; technology scales. Why materials innovation became the focus.

* 00:03:39 – The Thread: Physics → Symmetry → Machine Learning

* How gauge symmetry, group theory, and relativity informed equivariant neural networks.

* 00:06:52 – AI for Science Is Exploding (Not Emerging)

* The funding surge and why AI-for-Science feels like a new industrial era.

* 00:07:53 – Why Now? The Two Catalysts Behind AI for Science

* Protein folding, ML force fields, and the tipping point moment.

* 00:10:12 – How Engineers Can Enter AI for Science

* Practical pathways: curriculum, workshops, cross-disciplinary training.

* 00:11:28 – Why Materials Matter More Than Software

* The argument that everything—LLMs included—rests on materials innovation.

* 00:13:02 – Materials as a Search Engine

* The vision: automated exploration of chemical space like querying Google.

* 01:14:48 – Inside CuspAI: The Platform Architecture

* Generative models + multi-scale digital twin + experiment loop.

* 00:21:17 – Automating Chemistry: Human-in-the-Loop First

* Start manual → modular tools → agents → increasing autonomy.

* 00:25:04 – Moonshots vs Incremental Wins

* Balancing lighthouse materials with paid partnerships.

* 00:26:22 – Why Breakthroughs Will Still Require Humans

* Automation is vertical-specific and iterative.

* 00:29:01 – What Is Equivariance (In Plain English)?

* Symmetry in neural networks explained with the bottle example.

* 00:30:01 – Why Not Just Use Data Augmentation?

* The optimization trade-off between inductive bias and data scale.

* 00:31:55 – Generative AI Meets Stochastic Thermodynamics

* His upcoming book and the unification of diffusion models and physics.

* 00:33:44 – When the Book Drops (ICLR?)

Transcript

Max: I want to think of it as what I would call a physics processing unit, like a PPU, right? Which is you have digital processing units and then you have physics processing units. So it’s basically nature doing computations for you. It’s the fastest computer known, as possible even. It’s a bit hard to program because you have to do all these experiments. Those are quite bulky, it’s like a very large thing you have to do. But in a way it is a computation and that’s the way I want to see it. You can do computations in a data center and then you can ask nature to do some computations. Your interface with nature is a bit more complicated. But then these things will have to seamlessly work together to get to a new material that you’re interest