108 citations · 287 across the 10 of their papers we have counts for
10 papers
Designing Quantum Error Correcting Codes to fit decoders via Reinforcement Learning
Omer S. Sella, Robert Pinsler, Thomas Heinis
We present a reinforcement learning (RL) approach to the co-design of stabilizer sets of Quantum Error Correcting Codes (QECCs) and decoders. We show how to produce a generative mo…
MatterSim-MT: A multi-task foundation model for in silico materials characterization
Han Yang, Xixian Liu, Chenxi Hu +25
Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progres…
Probing the Limit of Heat Transfer in Inorganic Crystals with Deep Learning
Jielan Li, Zekun Chen, Qian Wang +21
Heat transfer is a fundamental property of matter. Research spanning decades has attempted to discover materials with exceptional thermal conductivity, yet the upper limit remains…
UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
Gongbo Zhang, Yanting Li, Renqian Luo +31
Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from crit…
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
Han Yang, Chenxi Hu, Yichi Zhou +19
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions p…
MatterGen: a generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner +18
The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative mo…
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang +6
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accur…
Symmetry-Aware Actor-Critic for 3D Molecular Design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi +1
Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging gra…
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics
Gregor N. C. Simm, Robert Pinsler, José Miguel Hernández-Lobato
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds. Existing approaches work with molecula…
Bayesian Batch Active Learning as Sparse Subset Approximation
Robert Pinsler, Jonathan Gordon, Eric Nalisnick +1
Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic…