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Predicting CO~2~ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks by Yue Jian; Yuyang Wang; Amir Barati Farimani is a Engineering article available to read on EtoBox.

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Ionic liquids (ILs) provide a promising solution for CO 2 capture and storage to mitigate global warming. However, identifying and designing the high-capacity IL from the giant chemical space require expensive and exhaustive simulations and experiments. Machine learning (ML) can accelerate the process of searching for desirable ionic molecules through accurate and efficient property predictions in a data-driven manner. However, existing descriptors and ML models for the ionic molecule suffer from the inefficient adaptation of molecular graph structure. Besides, few works have investigated the explainability of ML models to help understand the learned features that can guide the design of efficient ionic molecules. In this work, we develop both fingerprint-based ML models and graph neural networks (GNNs) to predict the CO 2 absorption in ILs. Fingerprint works on graph structure at the feature extraction stage, while GNNs directly handle molecule structure in both the feature extraction and model prediction stage. We show that our method outperforms previous ML models by reaching a high accuracy (MAE of 0.0137, R 2 of 0.9884). Furthermore, we take the advantage of GNN representation

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Author

Yue Jian; Yuyang Wang; Amir Barati Farimani

Publisher

American Chemical Society (ACS)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)