3 citations · 4 across the 12 of their papers we have counts for
8 papers · 1 filter
Grand Canonical Generators
Andreas Burger, Malte Franke, Luka Mucko +2
We introduce Grand Canonical Generators (GCG), a generative framework that extends Boltzmann generators to the grand canonical ensemble. We present two designs. The first condition…
From Global to Factor-Wise Expert Composition in Discrete Diffusion Models
Haozhe Huang, Yudong Xu, Abhijoy Mandal +1
Discrete diffusion models offer a powerful framework for solving complex reasoning tasks, particularly through compositional generation, which combines multiple pre-trained experts…
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
Yifan Ruan, Chenyang Cao, Andreas Burger +7
Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy…
De novo molecular generation with optical property preconditioning at the token level
Haozhe Huang, Manuel Gonzalez Lastre, Hyun Suk Park +3
Designing OLED molecules with targeted optical properties remains challenging due to the scarcity of high-quality data and the limited reliability of conditional control in generat…
MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4
Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and respo…
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupl…
Learning with springs and sticks
Luis Mantilla Calderón, Alán Aspuru-Guzik
Learning is a physical process. Here, we aim to study a simple dynamical system composed of springs and sticks capable of arbitrarily approximating any continuous function. The mai…
Symmetry From Scratch: Group Equivariance as a Supervised Learning Task
Haozhe Huang, Leo Kaixuan Cheng, Kaiwen Chen +1
In machine learning datasets with symmetries, the paradigm for backward compatibility with symmetry-breaking has been to relax equivariant architectural constraints, engineering ex…