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

What It Covers

Prof. Max Welling, co-founder of CuspAI, explains how his company uses AI-driven platforms to search the entire space of possible materials — not just known ones — to accelerate solutions for climate change, carbon capture, and the energy transition, combining generative models, digital twins, and high-throughput experimentation.

Key Questions Answered

• •Materials as the foundation layer: Every technology stack ultimately depends on physical materials — GPUs require novel etching materials, batteries and solar panels (currently ~22% efficiency, theoretically 50% with perovskite layers) are pure materials problems. AI engineers seeking real-world impact should reframe their work toward this foundational layer rather than software-only applications.
• •Physics Processing Units (PPU): Welling frames physical experiments not as validation endpoints but as computational units running in parallel with digital simulations. CuspAI's platform routes cheap computational screening first, eliminates poor candidates, then escalates to expensive experiments — treating nature itself as a fast, programmable co-processor alongside data center compute.
• •Platform architecture — generative + multi-fidelity digital twin: CuspAI's platform pairs a generative candidate model with a multi-scale, multi-fidelity digital twin that filters candidates through progressively expensive evaluation steps. LLM-based agents now autonomously orchestrate literature search and workflow execution, built incrementally by first running workflows manually, then automating piece by piece.
• •Equivariance vs. data augmentation trade-off: Hard-coding rotational or permutation symmetry into neural networks reduces training data requirements but can complicate the optimization landscape. When datasets are large, data augmentation sometimes outperforms hard-coded equivariance. The practical rule: use equivariance constraints when data is scarce and the symmetry is exact; prefer augmentation at scale.
• •Industrial partnership as prerequisite for materials breakthroughs: CuspAI only invests in a new material vertical after securing a domain-expert industrial partner, such as their PFAS water-filtration work with Chimera. Each new material class requires retraining models and redesigning experimental setups, so deep partnerships — not one-off contracts — provide the feedback loops needed to reach breakthrough results.