🔬Max Welling: Materials Underlie Everything

In this episode recorded at NeurIPS 2025, Max Welling traces the intellectual thread connecting quantum gravity, equivariant neural networks, diffusion models, and climate-focused materials discovery. 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 Welling 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. Timestamps 00:00 Introduction to Max Welling and the concept of Physics Processing Units (PPUs) 01:34 Max’s career evolution: From quantum gravity to climate-focused AI 03:39 Physics as the "thread": Symmetries, gauge theory, and stochastic thermodynamics 07:05 The explosion of "AI for Science" and the emerging investment bubble 07:53 Successes in protein folding and machine learning inter-atomic potentials 11:05 Why materials matter: The physical foundation of the AI software layer 13:47 Transforming material discovery into a search engine problem 14:47 The origin and mission of CuspAI: Solving carbon capture 17:49 CuspAI’s platform architecture: Generative models, digital twins, and agents 20:47 The role of humans in the loop: Moving from