ai for science

Jonathan Gorard ✅ @getjonwithit · 21h
We're immensely excited to be partnering with @SimonDBarnett and @zavaindar of @_DimensionCap, @TaylorCSargent of @IndustriousVC, and @blader, as we deliver on the promise of formally verifying the physical universe, and of closing the last remaining gaps between the computational, mathematical, and physical worlds.

I wrote a short post about this exceptional group of people, and why we're so thrilled to be working with them, as we continue to build Lanyon. Link below 👇

[quoted tweet]
Lanyon AI @lanyon_ai · 21h
Last month, right around the time we officially came out of stealth, we also closed our initial $10.6 million fundraising round, led by @_DimensionCap, with participation from @IndustriousVC....

[article card]
Lanyon AI Emerges from Stealth to Build the Future of Scientific and Technical Computing
AP | Updated Mon, August 17, 2026 at 9:01 AM GMT+2
[photo of three men standing against a brick wall, one holding a hat and umbrella prop]
$10.6 million fundraising round led by Dimension backs a team of world-leading Princeton mathematicians and physicists building a radically new kind of scientific AI backed by mathematical proofs of correctness.

startupsformal verificationai for sciencefundingtwitter

Shashwat Goel @ShashwatGoel7 · 29m
if you're using agents to do science, pls adversarially battle test with the help of agents before releasing as well.

so many cases where just asking claude/gpt what is wrong can surface issues

[quoted tweet]
Christopher Potts @ChrisGPotts · 42m
Every successful scientific project eventually enters a battle-testing phase in which the team is actively trying to show that their results don't hold. In a new post, @mmooritz and I describe how to ensure that this key process flourishes i...

ai agentsscientific methodologyai for science

continuation, end of thread @ProfBuehlerMIT

2026-08-03 — saved image

[continuing from previous screenshot]
...assumptions accurately capture physical reality remains an empirical question. That is why we fabricated and tested the results.

We generated four actuator classes by crossing two stimuli - humidity and heat - with two responses: bending and twisting. The fourth, thermal twisting, required no new pipeline and no separate derivation within the framework. It emerged by composing a thermal stimulus module already validated in one case with a twisting module validated in another. The generated G-code produced the intended motion without manual redesign, and all four predictions fell within one experimental standard deviation of the measured response.

Why this matters:

1. For AI in science, this provides a physics-aware type system against which generative proposals can be checked - and rejected at the interface - before expensive simulation, fabrication, or experiment. It is roughly analogous to proof checking, but for the composition of physical mechanisms.
2. For engineering, the accessible design space can scale with a library of validated components rather than with the number of individually derived cases.
3. The mathematics, category theory, carries all the way into a physical object on a print bed. This points toward scientific knowledge as executable infrastructure: models that are not only described in papers, but typed, composable, verifiable, and able to compile into experiments.

Excellent work led by my student @leemmarom with @SkylarTibbits & @GioeleZardini.

ai for sciencematerials sciencecategory theorybioinspired engineeringtwittermit

continuation, end of thread @ProfBuehlerMIT

2026-08-03 — saved image

Markus J. Buehler [verified] @ProfBuehlerMIT
Can we compile matter - for instance, a pine cone - and derive new active materials, end-to-end from observation to manufacturing? If physical systems can be formalized as composable mathematics, we can point AI that has been shown to resolve long-open mathematical problems at matter itself.  Our new work turns bioinspired engineering from analogy into formal compilation: biology and mechanics become explicit, checkable, and executable, so AI reasoning can produce physical designs.

This is the first end-to-end demonstration in which a formally compositional multiscale model is carried from a biological hierarchy, through engineered design and fabrication specification, to executable manufacturing code - and then to a physically tested artifact.

Background:

Humans have long been inspired by biology to advance technology, but this has usually been an ad hoc process rather than a mathematically rigorous one. Natural materials such as pinecones achieve adaptive behavior through mechanisms organized across many scales. Engineering typically translates those mechanisms by analogy: identify a biological principle, build something inspired by it, and validate each new design as a separate case. This can produce remarkable results, but the knowledge does not readily compound. Instead, we represent each scale as a dynamical module with explicit states, stimuli, governing laws, and interfaces. Every [cut off]

ai for sciencematerials sciencebioinspired engineeringtwittermit

continuation, end of thread @ProfBuehlerMIT

2026-08-03 — saved image

[continuing from previous screenshot]
...advance technology, but this has usually been an ad hoc process rather than a mathematically rigorous one. Natural materials such as pinecones achieve adaptive behavior through mechanisms organized across many scales. Engineering typically translates those mechanisms by analogy: identify a biological principle, build something inspired by it, and validate each new design as a separate case. This can produce remarkable results, but the knowledge does not readily compound. Instead, we represent each scale as a dynamical module with explicit states, stimuli, governing laws, and interfaces. Every scale-to-scale map must preserve the stimulus-response dynamics: evolve the fine-scale system and then map upward, or map upward first and then evolve. The two paths must agree. Because this condition is preserved under composition, locally valid interfaces remain consistent when assembled into the full hierarchy.

We then carry that structure into an engineered system, translate the target behavior into a verified fabrication specification, and compile it into G-code: the toolpaths, deposition sequence, temperatures, speeds, and other commands executed by a 3D printer. The intermediate translations are explicit, checkable, and executable rather than completed through an ad hoc handoff.

The formal guarantee is that given valid local models and interfaces, their composition remains valid. Whether those models and manufacturing assumptions accurately capture physical reality remains an empirical question. That is why we fabricated and tested the results.

ai for sciencematerials sciencebioinspired engineeringtwittermit