Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators. (arXiv:2211.04598v1 [cs.LG])

The demonstrated success of transfer learning has popularized approaches that
involve pretraining models from massive data sources and subsequent finetuning
towards a specific task. While such approaches have become the norm in fields
such as natural language processing, implementation and evaluation of transfer
learning approaches for chemistry are in the early stages. In this work, we
demonstrate finetuning for downstream tasks on a graph neural network (GNN)
trained over a molecular database containing 2.7 million water clusters. The
use of …

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