Search Results for author:
Found 6 papers, 3 papers with code
MatterGen: a generative model for inorganic materials design
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
Symmetry-Aware Actor-Critic for 3D Molecular Design
Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials.
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds.
Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models.
Factored Contextual Policy Search with Bayesian Optimization
Our key observation is that experience can be directly generalized over target contexts.
