Thiparat Chotibut
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thipchotibut.bsky.social
Thiparat Chotibut
@thipchotibut.bsky.social
Physicist, Dog-lover, Guitarist / Stat Mech + Machine Learning + Quantum Info = Research Interests / In the land of smiles 🇹🇭🤠😬
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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)! 🌪️
July 1, 2025 at 2:51 PM
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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.
July 1, 2025 at 2:51 PM
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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
July 1, 2025 at 2:51 PM
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/...
January 18, 2025 at 5:40 PM
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.
January 16, 2025 at 11:46 AM
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.
January 16, 2025 at 11:46 AM
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
January 16, 2025 at 11:46 AM
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!
January 16, 2025 at 11:46 AM