Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning-Reference-Cited by-同舟云学术

Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning

Published:2023 Issue:3 Volume:2 Page:591-601
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Chen Guzhong¹²^ORCID,Song Zhen¹^ORCID,Qi Zhiwen¹^ORCID,Sundmacher Kai²³^ORCID

Affiliation:

1. State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China

2. Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany

3. Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany

Abstract

We are introducing ILTransR, a transfer learning based one-stop framework to predict ionic liquid (IL) properties. High accuracy can be achieved by pre-training the model on millions of unlabeled data and fine-tuning on limited labeled data.

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00040K

Reference57 articles.

1. Ionic liquids: Innovative fluids for chemical processing

2. Ionic Liquids--Solvents of the Future?

3. Ionic Liquids as Modifying Agents for Protein Separation in Centrifugal Partition Chromatography

4. Mixtures of ionic liquids

5. Applications of ionic liquids in the chemical industry

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