Generating QM1B with PySCF$_{\text{IPU}}$. (arXiv:2311.01135v1 [cs.LG])

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 with billions of training examples. 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. These datasets are limited in size because the labels are
computed using the accurate (but computationally demanding) predictions of
Density Functional …

and natural language processing arxiv benefits chemistry computer computer vision datasets deep learning emergence examples foundation ipu language language processing natural natural language natural language processing processing progress quantum small tasks text training vision