Available on-demand - S.CT01.07.11
Transferable Neural Networks for High-Throughput Discovery of Supramolecular Chemistries
Wujie Wang1,William Harris1,Rafael Gomez-Bombarelli1
Massachusetts Institute of Technology1
Neural network potentials based on atomic embeddings have demonstrated accurate force and energy evaluations to simulate physical systems [1-5]. Moreover, it also shows good force field transferability across the chemical space of interests, which are critical for efficient computational screening. We propose an active learning strategy  that accelerates the sampling and training of transferable neural networks, particularly to capture supramolecular interactions that are critical for applications like designing chelating agents and electrolytes. The proposed method combines deep neural network training and active sampling of both chemical and configurational space in an end-to-end fashion to leverage the transferable configurational information across different but chemically similar species. This workflow has also enabled a high throughput virtual screening over the Ether-ion chemical space, which consists of 10^3 ~ 10^4 molecules, and successfully discovered candidate highly ion-conducting Ether molecules for organic electrolyte applications[7-10].
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