160 citations · 834 across the 51 of their papers we have counts for
53 papers
FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff +2
Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Fey…
Morphology and depletion force-based large-scale self-assembly of nanocubes on surface
Yeonhee Lee, Seungsang Cha, Yuna Kwak +3
Self-assembling nanoparticles is a highly efficient and facile way to form functional nano-, micro- and macrostructures. However, currently available methods lack precision and con…
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…
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…
The cost of quantum algorithms for biochemistry: A case study in metaphosphate hydrolysis
Ryan LaRose, Antonios M. Alvertis, Alan Bidart +10
We evaluate the quantum resource requirements for ATP/metaphosphate hydrolysis, one of the most important reactions in all of biology with implications for metabolism, cellular sig…
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…
Quantifying and minimizing dissipation in a non-equilibrium phase transition
Yuejun Shen, Zhiqiao Jiang, Yunfan Huang +4
In a finite-time continuous phase transition, topological defects emerge as the system undergoes spontaneous symmetry breaking. The Kibble-Zurek mechanism predicts how the defect d…
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…
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…
Minimally dissipative multi-bit logical operations
Jérémie Klinger, Grant M. Rotskoff
Modern computing architectures are vastly more energy-dissipative than fundamental thermodynamic limits suggest, motivating the search for principled approaches to low-dissipation…
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…
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
Yinuo Ren, Haoxuan Chen, Yuchen Zhu +5
Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis.…
Scaling field-theoretic simulation for multi-component mixtures with neural operators
Emmit K. Pert, Clay H. Batton, Sherry Li +2
Multi-component polymer mixtures are ubiquitous in biological self-organization but are notoriously difficult to study computationally. Plagued by both slow single molecule relaxat…
Features are fate: a theory of transfer learning in high-dimensional regression
Javan Tahir, Surya Ganguli, Grant M. Rotskoff
With the emergence of large-scale pre-trained neural networks, methods to adapt such "foundation" models to data-limited downstream tasks have become a necessity. Fine-tuning, pref…
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff +1
Discrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for d…
Coacervation drives morphological diversity of mRNA encapsulating nanoparticles
Emmit K. Pert, Paul J. Hurst, Robert M. Waymouth +1
The spatial arrangement of components within an mRNA encapsulating nanoparticle has consequences for its thermal stability, which is a key parameter for therapeutic utility. The me…
Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning
Frank Hu, Michael S. Chen, Grant M. Rotskoff +2
Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR sp…
Committor guided estimates of molecular transition rates
Andrew R. Mitchell, Grant M. Rotskoff
The probability that a configuration of a physical system reacts, or transitions from one metastable state to another, is quantified by the committor function. This function contai…
Non-Clifford diagonalization for measurement shot reduction in quantum expectation value estimation
Nicolas P. D. Sawaya, Daan Camps, Ben DalFavero +3
Estimating expectation values on near-term quantum computers often requires a prohibitively large number of measurements. One widely-used strategy to mitigate this problem has been…
Discrete generative diffusion models without stochastic differential equations: a tensor network approach
Luke Causer, Grant M. Rotskoff, Juan P. Garrahan
Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using…
Computing Nonequilibrium Responses with Score-shifted Stochastic Differential Equations
Jérémie Klinger, Grant M. Rotskoff
Using equilibrium fluctuations to understand the response of a physical system to an externally imposed perturbation is the basis for linear response theory, which is widely used t…
Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning
Abid Khan, Prateek Vaish, Yaoqi Pang +8
The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and bioch…
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
Haoxuan Chen, Yinuo Ren, Lexing Ying +1
Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and \emph{evaluate}, reducing the infer…
Aligning Transformers with Continuous Feedback via Energy Rank Alignment
Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran +1
Searching through chemical space is an exceptionally challenging problem because the number of possible molecules grows combinatorially with the number of atoms. Large, autoregress…