Siyuan Chen

Excited to share GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training, which received the Best Paper Award at the ICLR 2026 Workshop on Foundation Models for Science. Can we scale neural physics simulation without scaling expensive solver-generated labels? (1/6) (The below results are all predicted by GeoPT.)

@GuoMh14 @peterchencyc Congrats!

What happens when differentiable simulation meets diffusion models? You get a foundation model that is both 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 AND 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝗱 𝘁𝗼 𝗼𝗯𝗲𝘆 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀. 📢 Excited to share our latest work accepted to #ICLR2026: "Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection." 🧬 Foundation models like AlphaFold3 and Boltz have transformed biomolecular structure prediction — yet they still hallucinate physically invalid structures: steric clashes, distorted covalent geometry, broken stereochemistry. The root cause? Current predictors are trained to match empirical distributions — they never enforce physical validity as a 𝗵𝗮𝗿𝗱 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁. The secret sauce? A 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗯𝗹𝗲 𝗚𝗮𝘂𝘀𝘀-𝗦𝗲𝗶𝗱𝗲𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝘂𝗹𝗲 that takes provisional atom coordinates from the diffusion model and projects them onto the nearest physically valid configuration. By exploiting the locality and sparsity of atomic constraints, it converges stably and fast at scale. The module plugs into existing frameworks end-to-end via implicit differentiation — treating physical validity as a first-class citizen during 𝗯𝗼𝘁𝗵 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲. With our projection module in place, just 𝟮 𝗱𝗲𝗻𝗼𝗶𝘀𝗶𝗻𝗴 𝘀𝘁𝗲𝗽𝘀 suffice to match the structural accuracy of 200-step diffusion baselines — delivering a ~𝟭𝟬× 𝘄𝗮𝗹𝗹-𝗰𝗹𝗼𝗰𝗸 𝘀𝗽𝗲𝗲𝗱𝘂𝗽 while guaranteeing physical validity. Across six benchmarks (CASP15, PoseBusters, AF3-AB, dsDNA, RNA-Protein, and more), our model closes the gap between guaranteed physical validity and state-of-the-art structural accuracy. Joint work across UBC, MIT, NVIDIA, PKU, U of Utah, and Foundry Biosciences. 📄 Project: chensiyuan030105.github.io/ProteinGS/ 💻 Code: github.com/chensiyuan0301…

Modern all-atom biomolecular foundation models can be impressively accurate, but they still often generate steric clashes and other physically invalid structures. In our ICLR 2026 paper, we ask: can physical validity be enforced as a hard constraint, instead of a soft preference? Many existing approaches use physics mainly as inference-time guidance. That can reduce violations, but with finite guidance strength and finite denoising steps, invalid structures can still slip through. We wanted guarantees. (1/6)