Jonathan Godwin

19 papers A* 4Journal 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms. Trans. Mach. Learn. Res. Daniel T. Speckhard, Tim Bechtel, Sebastian Kehl, Jonathan Godwin, Claudia Draxl
2025	J	`jnl`	A practical guide to machine learning interatomic potentials - Status and future. CoRR Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood
2025	J	`jnl`	Analysis of static and dynamic batching algorithms for graph neural networks. CoRR Daniel T. Speckhard, Tim Bechtel, Sebastian Kehl, Jonathan Godwin, Claudia Draxl
2025	J	`jnl`	Mofasa: A Step Change in Metal-Organic Framework Generation. CoRR Vaidotas Simkus, Anders Christensen, Steven Bennett, Ian Johnson, Mark Neumann, James Gin, Jonathan Godwin, Benjamin Rhodes
2024	J	`jnl`	Orb: A Fast, Scalable Neural Network Potential. CoRR Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, Jonathan Godwin
2023	J	`jnl`	Band-gap regression with architecture-optimized message-passing neural networks. CoRR Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin, Claudia Draxl
2023	A*	`conf`	Pre-training via Denoising for Molecular Property Prediction. ICLR Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
2022	J	`jnl`	Learned Force Fields Are Ready For Ground State Catalyst Discovery. CoRR Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin
2022	A*	`conf`	Learned Simulators for Turbulence. ICLR 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
2022	J	`jnl`	Pre-training via Denoising for Molecular Property Prediction. CoRR Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Razvan Pascanu, Jonathan Godwin
2022	A*	`conf`	Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. ICLR Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia
2021	J	`jnl`	Automap: Towards Ergonomic Automated Parallelism for ML Models. CoRR Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov
2021	J	`jnl`	Graph Networks with Spectral Message Passing. CoRR Kimberly L. Stachenfeld, Jonathan Godwin, Peter W. Battaglia
2021	J	`jnl`	Large-scale graph representation learning with very deep GNNs and self-supervision. CoRR 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
2021	J	`jnl`	Learned Coarse Models for Efficient Turbulence Simulation. CoRR 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
2021	J	`jnl`	Very Deep Graph Neural Networks Via Noise Regularisation. CoRR Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia
2020	A*	`conf`	Learning to Simulate Complex Physics with Graph Networks. ICML Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia
2020	J	`jnl`	Learning to Simulate Complex Physics with Graph Networks. CoRR Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia
2016	J	`jnl`	Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies. CoRR Jonathan Godwin, Pontus Stenetorp, Sebastian Riedel