160 citations · 834 across the 51 of their papers we have counts for
13 papers · 1 filter
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…
Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems
Jérémie Klinger, Grant M. Rotskoff
Physical systems driven away from equilibrium by an external controller dissipate heat to the environment; the excess entropy production in the thermal reservoir can be interpreted…