Thiparat Chotibut

Curious about the scalability of analog VQAs that harness particular quantum phases of matter as an ansatz ⚛️? Check out our new study, led by a talented master’s student Kasidit - nice summary thread below.
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To stat mech crowd: think of congestion games as out-of-equilibrium many-body active matter.
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This is an exactly solvable active system (multi-agent RL) where microscopic chaos coexists with macroscopic ergodic convergence - check it out!
www.pnas.org/doi/10.1073/...

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✨ Remarkably, yet the long-run average number of agents on route 1 settles on the social-optimum / Nash equilibrium (bottom right) ⛳️, despite the day-to-day head-count of route 1 being provably chaotic (bottom left)! 🌪️

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Results: When some agents learn (adapt) very fast, their individual strategies turn chaotic 🌪️. Top panel - x axis: agent type with different learning rates, y-axis fraction of that agent selecting route 1.

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Ever wondered how tools from statistical physics can help understand learning in diverse reinforcement-learning populations?

Check out our new PNAS paper (Special Feature: Collective Artificial Intelligence & Evolutionary Dynamics) here pnas.org/doi/10.1073/...
#PNASNews

Check out our latest work spearheaded by a talented master student, Kasidit. See a summary thread below 👇

Wondering whether simply scaling up system sizes and cranking up chaos really supercharges a quantum reservoir, or if dissipation is really a resource at scale? 🌀

We demystify these QRC myths in our new preprint: [https://scirate.com/arxiv/2505.10080]

🧵 Key takeaways in a great thread below!👇

Published in #PNAS 🎉

Noise isn't just disruptive; it can enhance neural computations, especially in working memory tasks!

Biologically plausible RNNs harnessing noise also operate near the “edge of chaos,” supporting the critical brain hypothesis 🧠✨

Check out 👇
www.pnas.org/doi/10.1073/...

Kudos to the team (especially to Tang and Teerachote) for this computational feat powered by thousands of NVIDIA GPU hours and LOADs of trials and errors! But eventually they succeeded at rivaling state-of-the-art models!

Feedbacks are welcome!

Paper --> arxiv.org/abs/2501.08998

We show that CrystalGRW yields stable, unique, and novel structures (S.U.N. materials) close to their DFT ground states. The fun part for me is to revisit the theory of random walks on Riemannian manifolds and make this works for generative modeling.

If you’re interested in materials discovery or generative modeling, CrystalGRW might cut down the guesswork and skip expensive ab initio calculations and also let you specify, say, a target crystallographic point group or composition right off the bat.

The coolest part (in my humble opinion) is how it balances crystal symmetry requirements, periodicity, compositional constraints, and training stability in a single, unified generative modeling framework: diffusion models on natural Riemannian manifolds that suitably represent crystal properties

Hey everyone, just wanted to share our new preprint on crystal structure generation (CrystalGRW) is live on arXiv! We’ve been playing with diffusion-based models that treat crystal structures in their “natural domain” (Riemannian manifolds) (like Torus for coords capturing periodicity) and it works!