10 citations · 13 across the 9 of their papers we have counts for
9 papers
Self-Refining Training for Amortized Density Functional Theory
Majdi Hassan, Cristian Gabellini, Hatem Helal +2
Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by finding an approximate solution to the…
MESS: Modern Electronic Structure Simulations
Hatem Helal, Andrew Fitzgibbon
Electronic structure simulation (ESS) has been used for decades to provide quantitative scientific insights on an atomistic scale, enabling advances in chemistry, biology, and mate…
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
Alexander Mathiasen, Hatem Helal, Paul Balanca +6
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as . Schütt et al. (2019) successfully appro…
Generating QM1B with PySCF
Alexander Mathiasen, Hatem Helal, Kerstin Klaser +6
The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets w…
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
Dominic Masters, Josef Dean, Kerstin Klaser +9
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing compo…
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
Dominic Masters, Josef Dean, Kerstin Klaser +7
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task.…
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Hatem Helal, Jesun Firoz, Jenna Bilbrey +5
Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitivel…
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill +6
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task…
Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks
Mario Michael Krell, Manuel Lopez, Sreenidhi Anand +2
When processing a batch of graphs in machine learning models such as Graph Neural Networks (GNN), it is common to combine several small graphs into one overall graph to accelerate…