Search Results for author:
Found 7 papers, 4 papers with code
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
For comparison, Sch\"utt et al. (2019) spent 626 hours creating a dataset on which they trained their NN for 160h, for a total of 786h; our method achieves comparable performance within 31h.
Generating QM1B with PySCF$_{\text{IPU}}$
Similar benefits are yet to be unlocked for quantum chemistry, where the potential of deep learning is constrained by comparatively small datasets with 100k to 20M training examples.
GPS++: Reviving the Art of Message Passing for Molecular Property Prediction
We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction.
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Molecular property calculations are the bedrock of chemical physics.
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction
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.
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
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
However, the sizes of small graphs can vary substantially with respect to the number of nodes and edges, and hence the size of the combined graph can still vary considerably, especially for small batch sizes.
