Jonathan Godwin

According to our database, Jonathan Godwin authored at least 13 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Orb: A Fast, Scalable Neural Network Potential.

Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, Jonathan Godwin

CoRR, 2024

2023

Band-gap regression with architecture-optimized message-passing neural networks.

Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin, Claudia Draxl

CoRR, 2023

Pre-training via Denoising for Molecular Property Prediction.

Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learned Force Fields Are Ready For Ground State Catalyst Discovery.

Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin

CoRR, 2022

Learned Simulators for Turbulence.

Kimberly L. Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez

Proceedings of the Tenth International Conference on Learning Representations, 2022

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Learned Coarse Models for Efficient Turbulence Simulation.

Kimberly L. Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez

CoRR, 2021

Automap: Towards Ergonomic Automated Parallelism for ML Models.

Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov

CoRR, 2021

Large-scale graph representation learning with very deep GNNs and self-supervision.

Ravichandra Addanki, Peter W. Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, Petar Velickovic

CoRR, 2021

Very Deep Graph Neural Networks Via Noise Regularisation.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

CoRR, 2021

Graph Networks with Spectral Message Passing.

Kimberly L. Stachenfeld, Jonathan Godwin, Peter W. Battaglia

CoRR, 2021

2020

Learning to Simulate Complex Physics with Graph Networks.

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia

Proceedings of the 37th International Conference on Machine Learning, 2020

2016

Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies.

Jonathan Godwin, Pontus Stenetorp, Sebastian Riedel

CoRR, 2016