65 citations · 196 across the 12 of their papers we have counts for
13 papers
Diffeomorphic Optimization
Ludwig Winkler, Andrew Leaver-Fay, Joseph Kleinhenz +1
Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space. Optimizing differentiable objectives on this manifol…
Unified all-atom molecule generation with neural fields
Matthieu Kirchmeyer, Pedro O. Pinheiro, Emma Willett +7
Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBin…
Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment
Ameya Daigavane, YuQing Xie, Bodhi P. Vani +3
Diffusion models are a popular class of generative models trained to reverse a noising process starting from a target data distribution. Training a diffusion model consists of lear…
Concept Bottleneck Language Models For protein design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang +8
We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our arc…
JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Ameya Daigavane, Bodhi P. Vani, Darcy Davidson +3
Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Curre…
Inchworm quasi Monte Carlo for quantum impurities
Hugo U. R. Strand, Joseph Kleinhenz, Igor Krivenko
The inchworm expansion is a promising approach to solving strongly correlated quantum impurity models due to its reduction of the sign problem in real and imaginary time. However,…
Protein Discovery with Discrete Walk-Jump Sampling
Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny +10
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Mar…
3D molecule generation by denoising voxel grids
Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz +6
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from…
The Kondo Cloud in a 1D Nanowire
Joseph Kleinhenz, Igor Krivenko, Guy Cohen +1
A recent experiment [Nature 579, 210--213 (2020)] probed the extent of the Kondo cloud in 1D by measuring the effect of electrostatic perturbations applied a distance away from…
Dynamic Control of Nonequilibrium Metal-Insulator Transitions
Joseph Kleinhenz, Igor Krivenko, Guy Cohen +1
We demonstrate a first order metal-insulator phase transition in the repulsive, fully frustrated, single-band Hubbard model as a function of the coupling to a fermion bath. Time de…
Dynamics of Kondo voltage splitting after a quantum quench
Igor Krivenko, Joseph Kleinhenz, Guy Cohen +1
We analyze the time-dependent formation of the spectral function of an Anderson impurity model in the Kondo regime within a numerically exact real-time quantum Monte Carlo framewor…
Quantum Monte Carlo solution of the dynamical mean field equations in real time
Qiaoyuan Dong, Igor Krivenko, Joseph Kleinhenz +3
We present real-time inchworm quantum Monte Carlo results for single-site dynamical mean field theory on an infinite coordination number Bethe lattice. Our numerically exact result…
Currents and Green's functions of impurities out of equilibrium -- results from inchworm Quantum Monte Carlo
Andrey E. Antipov, Qiaoyuan Dong, Joseph Kleinhenz +2
We generalize the recently developed inchworm quantum Monte Carlo method to the full Keldysh contour with forward, backward, and equilibrium branches to describe the dynamics of st…