17 papers
Optimal parameterization of nonequilibrium generalized master equations from discrete-time experimental data
Chih-Wei Joshua Liu, Jérémie Klinger, Grant M. Rotskoff
Kinetic analyses of experiments often require coarse-grained descriptions, but complex systems rarely conform to the widely used modeling assumptions of Markovianity and thermodyna…
Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence
Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos +5
One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydro…
A Unified Approach to Analysis and Design of Denoising Markov Models
Yinuo Ren, Grant M. Rotskoff, Lexing Ying
Probabilistic generative models based on measure transport, such as diffusion and flow-based models, are often formulated in the language of Markovian stochastic dynamics, where th…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
Yinuo Ren, Wenhao Gao, Lexing Ying +2
We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods…
Scaling Transferable Coarse-graining with Mean Force Matching
Abigail Park, Shriram Chennakesavalu, Grant M. Rotskoff
Coarse-grained molecular dynamics often sacrifices accuracy and transferability for computational efficiency, but the use of machine learned potentials is helping coarse-grained mo…