The creation of elemental modes to represent atomic species enables transfer learning and lowers computational cost, bringing chemical modeling closer to a single machine learning model that can be fine-tuned for different systems.The creation of elemental modes to represent atomic species enables transfer learning and lowers computational cost, bringing chemical modeling closer to a single machine learning model that can be fine-tuned for different systems.
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A new way to represent atomic species improves molecular machine learning models
Semantic Scholar · Chemistry · 2019
Abstract
The creation of elemental modes to represent atomic species enables transfer learning and lowers computational cost, bringing chemical modeling closer to a single machine learning model that can be fine-tuned for different systems.The creation of elemental modes to represent atomic species enables transfer learning and lowers computational cost, bringing chemical modeling closer to a single machine learning model that can be fine-tuned for different systems.