Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies. - Jonathan Godwin; Pontus Stenetorp; Sebastian Riedel 001 (CoRR, 2016)

Learning to Simulate Complex Physics with Graph Networks. - Alvaro Sanchez-Gonzalez; Jonathan Godwin; Tobias Pfaff; Rex Ying; Jure Leskovec; Peter W. Battaglia (ICML, 2020)

Graph Networks with Spectral Message Passing. - Kimberly L. Stachenfeld; Jonathan Godwin; Peter W. Battaglia (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)

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)

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)

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)

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 (ICLR, 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 (ICLR, 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)

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 (ICLR, 2023)

Band-gap regression with architecture-optimized message-passing neural networks. - Tim Bechtel; Daniel T. Speckhard; Jonathan Godwin; Claudia Draxl (CoRR, 2023)

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)

Analysis of static and dynamic batching algorithms for graph neural networks. - Daniel T. Speckhard; Tim Bechtel; Sebastian Kehl; Jonathan Godwin; Claudia Draxl (CoRR, 2025)

A practical guide to machine learning interatomic potentials - Status and future. - 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; et al. (CoRR, 2025)