🔬 Max Welling: Searching the Space of All Possible Materials скачать в хорошем качестве

🔬 Max Welling: Searching the Space of All Possible Materials

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 manual workflows to automation 24:39 Strategy for breakthroughs: Lighthouse moonshots vs. incremental partnerships 28:40 Technical Deep Dive: Explaining Equivariance and symmetry in neural networks 31:07 The "Bitter Lesson" in the context of scientific inductive biases 31:47 Preview of "Generative AI and Stochastic Thermodynamics" (Upcoming Book)