Closed Loop Materials Discovery Compute Plus Experiment
Sources: 1 • Confidence: Medium • Updated: 2026-03-02 20:00
Key takeaways
- Max Welling proposes treating physical experiments as a "physics processing unit" (nature-as-compute) that should be integrated with data-center computation in a materials-discovery workflow.
- Max Welling states CuspAI was started about 20 months prior to the interview to develop technology for carbon dioxide removal motivated by climate-change concerns.
- Max Welling describes equivariance as hard-coding symmetry constraints (such as rotations, translations, permutations) into neural network weights to improve generalization across transformed inputs with less data.
- Max Welling describes AI-for-science (and science-for-AI) as an emerging discipline focused on the interface between physics/science and machine learning.
- Max Welling states his forthcoming technical book argues diffusion models and generative AI share identical mathematics with stochastic non-equilibrium thermodynamics and aims to enable cross-fertilization of methods between the fields.
Sections
Closed Loop Materials Discovery Compute Plus Experiment
- Max Welling proposes treating physical experiments as a "physics processing unit" (nature-as-compute) that should be integrated with data-center computation in a materials-discovery workflow.
- Max Welling describes CuspAI's platform as using a generative model to propose candidates and a multi-scale, multi-fidelity digital twin to filter candidates from cheap to expensive evaluations before experimental validation.
- Max Welling states CuspAI's automation approach starts with humans manually assembling modular tool workflows and then progressively automates components and sequencing using agents or optimizers.
- Max Welling states CuspAI has added agents that search chemical literature, propose experimental suggestions, and orchestrate computations and experiments.
- Max Welling states an expectation that domain experts will remain in the loop for a long time, with near-term goals focused on empowerment and acceleration rather than fully autonomous labs.
- Max Welling states an expectation that materials R&D can increasingly be approached as an automated search over the space of molecules/materials rather than a slow hypothesis-experiment loop.
Cuspai Company Scope And Commercialization Gates
- Max Welling states CuspAI was started about 20 months prior to the interview to develop technology for carbon dioxide removal motivated by climate-change concerns.
- Max Welling states CuspAI has grown to about 40 people and raised about 130 million in investment.
- Max Welling states CuspAI commits to new material-development directions only when it has a strong industrial partner to collaborate through deployment with domain expertise.
- Max Welling states he helped start CuspAI to work on climate change through technology while engaging with deep scientific problems in materials science.
- Max Welling states CuspAI is pursuing a mixed strategy consisting of a confidential long-term lighthouse material proof point alongside shorter paid projects such as training force fields for clients.
- Max Welling states CuspAI is working with Kemira on a water filtration project aimed at removing PFAS.
Physics Inspired Ml Inductive Bias And Training Tradeoffs
- Max Welling describes equivariance as hard-coding symmetry constraints (such as rotations, translations, permutations) into neural network weights to improve generalization across transformed inputs with less data.
- Max Welling states data augmentation can sometimes outperform hard-coded equivariance because constraints can make optimization harder and impede finding good minima.
- Max Welling states inductive bias trades off against data, and if the imposed bias is not exactly correct it can cap achievable performance.
- Max Welling states physics is the central thread linking his work and emphasizes the importance of symmetry concepts from theoretical physics to machine learning.
- Max Welling states his symmetry-to-ML work with Taco Cohen progressed from rotational symmetries to gauge symmetries on spheres and related domains.
Ai For Science Field Dynamics Capitalization And Bubble Risk
- Max Welling describes AI-for-science (and science-for-AI) as an emerging discipline focused on the interface between physics/science and machine learning.
- Max Welling attributes AI-for-science momentum to successful applications (including protein folding and machine-learning interatomic potentials) and to a belief that the field has reached a capability inflection point.
- Max Welling states there is a Jeff Bezos-backed AI-for-science startup that raised a 6.2 billion seed round.
- Max Welling states AI-for-science investment and activity are rapidly accelerating and may be forming a bubble.
Diffusion Models Linked To Non Equilibrium Thermodynamics
- Max Welling states his forthcoming technical book argues diffusion models and generative AI share identical mathematics with stochastic non-equilibrium thermodynamics and aims to enable cross-fertilization of methods between the fields.
- Max Welling claims diffusion models, reinforcement learning, Schrödinger bridges, and MCMC share mathematical structure with stochastic thermodynamics for non-equilibrium systems.
- Max Welling states he taught a course at the African Institute for Mathematical Sciences (near Cape Town) and turned it into a book that has been sent to a publisher.
- Max Welling states his upcoming book focuses on relationships among free energy, diffusion models (generative AI), and stochastic thermodynamics.
Watchlist
- Max Welling describes AI-for-science (and science-for-AI) as an emerging discipline focused on the interface between physics/science and machine learning.
Unknowns
- What are the measured iteration-time and throughput gains (end-to-end) from integrating experiments as a closed-loop compute component versus a conventional simulation-then-validation pipeline?
- What quantitative screening efficiency metrics exist for the multi-fidelity digital twin (e.g., hit rate at each fidelity tier, calibration error, compute cost per surviving candidate)?
- How mature and reliable are the agents used for literature search and experiment/computation orchestration (error rates, reproducibility outcomes, required human oversight)?
- What is the identity and verified financing structure of the asserted Jeff Bezos-backed AI-for-science startup with a 6.2 billion seed round?
- What are the concrete commercialization milestones and performance targets for CuspAI’s CO2 removal focus (capture performance, durability, operating conditions, and any pilot plans)?