🔬Searching the Space of All Possible Materials — Prof. Max Welling, CuspAI
Summary
- AI for science is “not just emerging, it’s exploding,” but Welling says the leap from hundreds of millions to billions also means “we are creating a new bubble here.” Protein folding and machine-learning interatomic potentials are successful examples; a Jeff Bezos startup’s cited $6.2 billion Series C is the capital-market extreme, while the opportunity moves AI beyond ad placement into drugs, energy materials, and carbon capture.
- Materials are the upstream constraint on nearly every technology thesis, including AI itself. An LLM depends on a GPU, whose wafer fabrication and EUV lithography become materials problems as conventional scaling reaches its limits; in energy, Welling contrasts solar’s roughly 22% capture today with a theoretical 50% using perovskite layers. “Underlying almost everything is a material.”
- The larger bet is to turn materials discovery from artisanal hypothesis-testing into a search engine over “the space of all possible molecules.” A user would specify what they want, while computation and remote experiments generate candidates and feedback; the opportunity extends from better batteries to plastics that decompose after weeks and become fertilizer.
- CuspAI, founded by Welling and Chet Edwards, has become a roughly 40-person company with $130 million raised after about 20 months. Its climate premise is demanding: staying within two degrees would require zero emissions by 2050, then another 50–100 years of atmospheric carbon removal at about half today’s emissions rate.
- The platform combines candidate generation, progressively costlier digital-twin screening, and experiments that Welling wants to treat as a “physics processing unit.” Cheap calculations eliminate obvious failures before multi-scale, multi-fidelity models and physical tests take over; agents increasingly search literature and orchestrate the workflow, while high-throughput experimentation is being added. Welling says the design is “not rocket science,” but implementation and data are “surprisingly hard”—and are the moat.
- Automation will advance through a “retreat” of expert intervention, not a near-term dark laboratory that removes chemists. Teams first assemble modular workflows manually, then automate bounded tasks such as choosing and validating the right DFT calculation; Welling does not expect complete automation of domain expertise in the next five years and expects chemists to keep judging candidate materials.
- Practical breakthroughs can arrive before full automation because each added capability is immediately useful. CuspAI is pursuing an undisclosed lighthouse material alongside narrower paid projects and a PFAS-removal partnership with Kamira; unlike fusion or quantum computing, the platform need not wait decades for an all-or-nothing payoff, though every new materials vertical still requires retraining and renewed human guidance.
Deep dive
1. AI for science is exploding—and already resembles a bubble
Max Welling said his early career followed an internal “sensor” for deep questions—black holes, universe boundaries, quantum mechanics. With retirement still roughly ten years away, impact became a second axis: two-dimensional quantum gravity might yield papers but little worldly effect, while materials could combine difficult science with climate leverage.
“Physics is the thread”: symmetries from particle physics and general relativity informed his equivariant machine-learning work, while diffusion models led him toward stochastic thermodynamics. He now sees common mathematics spanning reinforcement learning, Schrödinger bridges, MCMC sampling, and physical systems outside equilibrium.
His enduring fascination includes Gerard ’t Hooft’s claim that quantum mechanics is wrong and his proposed replacement, which Welling called courageous even though “nobody understands what he’s saying.” Welling’s own ambition is more modest: understand quantum mechanics without “all the weird multiverses and collapses.”
Protein folding and machine-learning force fields showed that modern AI could move scientific methodology. As researchers sought impact beyond “ad placement” and multimedia, investment climbed from hundreds of millions into billions; Welling cited a $6.2 billion Series C for a Jeff Bezos startup, called the field a virgin field, and conceded: “We are creating a new bubble here.”
2. Materials discovery is becoming a search problem
Welling’s stack runs downward from software to matter: beneath an LLM sits a GPU, and beneath that sit wafer materials patterned with EUV light. With dimensional scaling near its limits, further semiconductor improvement increasingly becomes “an actual material problem.”
The energy transition is likewise materials-bound through batteries, fuel cells, and solar panels. Welling’s concrete example was adding perovskite layers over silicon, potentially capturing “theoretically up to 50%” of light versus “I don’t know, maybe 22” today.
Traditional discovery proceeds slowly from papers to hypotheses, experiments, and revised understanding. Welling wants instead to “search the space of all possible molecules”—including molecules neither manufactured nor found in nature—then refine a query as computation and experiments return a list of candidates.
That search-engine framing extends to sustainability: Welling argued that plastics could plausibly be designed to destroy themselves after a few weeks and become fertilizer. “These things can be done, right? And we should do it.”
3. CuspAI links digital models to a physics processing unit
CuspAI began about 20 months earlier from Welling’s climate concern and was started with co-founder Chet Edwards. His premise for staying within two degrees requires emissions reaching zero by 2050, followed by 50–100 years of directly removing CO₂ at roughly half today’s emissions rate—an unsolved problem whose failure could mean four degrees, which he described as “very bad.”
The company grew to about 40 people and collected $130 million, unusually large by Welling’s European benchmark. Its platform generates candidates, then walks them through a multi-scale, multi-fidelity digital-twin ladder: “Do the cheap things first,” discard obvious failures, reserve expensive calculations for survivors, and send the final few into experiments for feedback.
Literature-search agents and autonomous orchestration are being added at various levels of maturity. LLMs are being integrated, and CuspAI is moving toward high-throughput experimentation; Welling said self-driving-lab capabilities should also be added. He wants to treat experiments as a “physics processing unit”—nature, “possibly” the fastest computer known, computing alongside the data center.
Welling’s distinction is sharp: the architecture is “not rocket science,” but obtaining data and making the whole platform work is “surprisingly hard.” He said the moat is in the data the company can get its hands on and in actually building the platform.
4. Humans and industrial partners remain inside the loop
Automation starts with modular tools assembled manually by chemists. If a promising porous material collapses when shaken, the team adds a stability test; only after the workflow is understood might a Bayesian optimizer or an LLM trained to be a good chemist learn which tools to invoke and in what order.
DFT illustrates the gradual “retreat” of expertise: today a non-specialist consults an expert about the right DFT calculation for the problem, how long to run it, and whether the result is good; software might automate those bounded decisions. The eventual interface may return candidate materials from a query, but the chemist still decides what is actually good.
One host tested the stronger vision of a fully automated process. Welling rejected a “completely dark lab” that receives “find something interesting” and independently defines interestingness: automating away lifelong domain expertise “in the next five years? I don’t think that’s going to happen at all.”
CuspAI only starts investing in a materials direction after finding a good industrial partner. It is pursuing an undisclosed lighthouse material as an AI-enabled real-world proof point, while accepting narrower force-field projects; its PFAS water-filtration work with Kamira exemplifies a deep partnership. Each capability is immediately useful, but new verticals require different experiments, instruments, retrained and fine-tuned models, and renewed human direction.
5. Physics priors help, but the bitter lesson survives
Welling explained equivariance with a rotated bottle: an ordinary network may have to relearn every orientation, while a symmetry-aware network generalizes from one, reducing data needs. Rotations, translations, and graph permutations can be built into the model as constraints on its weights.
The host’s pushback—worth keeping: why not use data augmentation? Welling conceded that augmentation sometimes works better because exact equivariance complicates the optimization surface; encoding a true symmetry saves data, but an imperfect constraint can make good minima harder to find.
His synthesis was “a trade-off between data and inductive bias.” A wrong bias imposes a performance ceiling, and the applicable bitter lesson from LLMs is that architectures must scale unless the dataset is tiny.
Welling’s forthcoming Generative AI and Stochastic Thermodynamics, which he said he had sent to the publisher, develops the same cross-fertilization. It maps generative AI onto nonequilibrium statistical mechanics, connecting variational free energy, Geoff Hinton and Radford Neal’s work, Karl Friston’s free-energy principle, and fluctuation theorems. He hoped to have it in hand for an ICLR keynote in April, while stressing that publication timing remained outside his control.