Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks. - Mario Michael Krell; Manuel Lopez Roldan; Sreenidhi Anand; Hatem Helal; Andrew William Fitzgibbon (CoRR, 2022)

Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators. - Jenna A. Bilbrey; Kristina M. Herman; Henry Sprueill; Sotiris S. Xantheas; Payel Das; Manuel Lopez Roldan; Mike Kraus; Hatem Helal; Sutanay Choudhury (CoRR, 2022)

Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry. - Hatem Helal; Jesun Firoz; Jenna A. Bilbrey; Mario Michael Krell; Tom Murray; Ang Li 006; Sotiris S. Xantheas; Sutanay Choudhury (CoRR, 2022)

GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Klaser 001; Zhiyi Li; Sam Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Ladislav Rampásek; Dominique Beaini (CoRR, 2022)

Generating QM1B with PySCFIPU. - Alexander Mathiasen; Hatem Helal; Kerstin Klaser 001; Paul Balanca; Josef Dean; Carlo Luschi; Dominique Beaini; Andrew W. Fitzgibbon; Dominic Masters (NeurIPS, 2023)

GPS++: Reviving the Art of Message Passing for Molecular Property Prediction. - Dominic Masters; Josef Dean; Kerstin Kläser 001; Zhiyi Li; Samuel Maddrell-Mander; Adam Sanders; Hatem Helal; Deniz Beker; Andrew W. Fitzgibbon; Shenyang Huang; Ladislav Rampásek; Dominique Beaini (Trans. Mach. Learn. Res., 2023)

Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory. - Alexander Mathiasen; Hatem Helal; Paul Balanca; Adam Krzywaniak; Ali Parviz; Frederik Hvilshøj; Blazej Banaszewski; Carlo Luschi; Andrew William Fitzgibbon (CoRR, 2024)

MESS: Modern Electronic Structure Simulations. - Hatem Helal; Andrew W. Fitzgibbon (CoRR, 2024)

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units. - Hatem Helal; Jesun Firoz; Jenna A. Bilbrey; Henry Sprueill; Kristina M. Herman; Mario Michael Krell; Tom Murray; Manuel Lopez Roldan; Mike Kraus; Ang Li 006; Payel Das; Sotiris S. Xantheas; et al. (J. Chem. Inf. Model., 2024)

Self-Refining Training for Amortized Density Functional Theory. - Majdi Hassan; Cristian Gabellini; Hatem Helal; Dominique Beaini; Kirill Neklyudov (CoRR, 2025)