Material Progress

Orbital Materials’ core bet is that AI can turn materials discovery from artisanal trial-and-error into a more predictable engineering discipline. Its generative models propose candidates, force-prediction models qualify them, and an internal wet lab closes the loop. Jonathan Godwin says that by the time they decide what to make, they have answered roughly 90% of the questions needed for confidence and narrowed the field to two or three materials.

The deepest technical signal is out-of-distribution generalization from roughly 20-atom inorganic crystals to a complex protein system. Jonathan calls it evidence that the models are “learning something really fundamental at that small scale.” Message-passing neural networks do not use explicit positional embeddings like language models, allowing atomistic simulations to grow with available computing resources, subject to practical limits.

The potassium-channel work offers a striking but explicitly unconfirmed demonstration of that generalization. On one V100 GPU, Tim Duignan observed water entering the channel and a hydroxyl group drawing it in through a hydrogen bond—a candidate explanation for mutation experiments in which removing that group reduces conductivity by almost an order of magnitude. The work had not yet been peer-reviewed or experimentally confirmed; Tim describes it as a strong, plausible hypothesis.

Orbital’s initial commercialization focus is the data-center buildout rather than moonshot materials such as room-temperature superconductors. It is developing thermal-management and decarbonization materials, including a CO2-capture material designed to use data-center waste heat and airflow. The economic fulcrum is regeneration: one-off capture is relatively solved, while lowering desorption energy and reusing a material for perhaps 10 years could radically reduce costs.

The model economics resemble image diffusion more than frontier language models. Orbital says it has built leading models and a wet lab with less than $40 million raised. Atomistic simulations can scale across additional chips because the architecture has no Transformer-style positional limit, though large systems become slow, long-range electrostatics may require additional modeling, and density functional theory is inadequate for some highly correlated electron systems.

The defensibility may come from combining scalable models with tacit experimental knowledge that has historically been difficult to copy. Jonathan compares semiconductor process expertise—perhaps 10,000 steps, transmitted through a master-and-apprentice process—with TSMC’s accumulated advantage. Orbital’s internal lab therefore matters less as manufacturing capacity than as a fast feedback system that keeps its AI grounded in manufacturable materials.

Higher scientific output may come with a painful redistribution of satisfying work from humans to machines. A study summarized by the host reported 44% more materials discovered, 39% more patent filings and 177% more downstream product innovation, while 82% of scientists reported lower job satisfaction as AI handled more idea generation and humans did more validation. Jonathan’s forecast is that “creativity is one of the first things that’s going to go,” even as he argues accelerated discovery is necessary to produce more for less.

🔗 Original source & video: Material Progress: Developing AI’s Scientific Intuition, with Orbital Materials’ Jonathan & Tim