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09/12/2023
Band-gap regression with architecture-optimized message-passing neural networks
Graph-based neural networks and, specifically, message-passing neural ne...
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09/26/2022
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (“DFT”) force...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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12/31/2021
Learned Coarse Models for Efficient Turbulence Simulation
Turbulence simulation with classical numerical solvers requires very hig...
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12/06/2021
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures...
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07/20/2021
Large-scale graph representation learning with very deep GNNs and self-supervision
Effectively and efficiently deploying graph neural networks (GNNs) at sc...
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06/15/2021
Very Deep Graph Neural Networks Via Noise Regularisation
Graph Neural Networks (GNNs) perform learned message passing over an inp...
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12/31/2020
Graph Networks with Spectral Message Passing
Graph Neural Networks (GNNs) are the subject of intense focus by the mac...
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02/21/2020
Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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12/29/2016
