Of central importance to evaluate the suitability of ionic liquids (ILs)
for a process is the accurate estimation of IL properties related to
target performances. In this work, a versatile deep learning method for
predicting IL properties is developed. Molecular fingerprints are
derived from the encoder state of a Transformer model pre-trained on the
PubChem database, which allows transfer learning from large-scale
unlabeled data and significantly improves generalization performance for
developing models with small datasets. Employing the pre-trained
molecular fingerprints, convolutional neural network (CNN) models for IL
properties prediction are trained and tested on 11 databases. The
obtained Transformer-CNN models present superior performance to
state-of-the-art models in all cases and enable property prediction of
millions of ILs shortly. The application of the proposed models is
exemplified by searching CO2 absorbent from a huge database of 8,333,096
synthetically feasible ILs, which is by far the most high-throughput IL
screening in literature.
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