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Machine-Learning-Enabled Ligand Screening for Cs/Sr Crystallizing Separation.

Bingbing Wang¹, Zhiyuan Zhang¹, Yue Dong¹

¹School of Chemical Engineering, Sichuan University, Chengdu 610065, China.

Inorganic Chemistry

|August 9, 2023

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Researchers developed a computational framework to efficiently identify ligands for nuclear fuel reprocessing. This method accelerates the discovery of novel materials for critical separation processes, enhancing nuclear energy sustainability.

Area of Science:

Nuclear Chemistry
Materials Science
Computational Chemistry

Background:

Sustainable nuclear energy relies on efficient spent nuclear fuel reprocessing.
Current separation technologies face challenges, necessitating advanced materials.
Selective coordination of metal cations in liquid waste is crucial.

Purpose of the Study:

To develop a high-throughput screening framework for identifying effective ligands for nuclear waste separation.
To improve the efficiency of discovering next-generation separation materials.
To address challenges in spent nuclear fuel reprocessing.

Main Methods:

Utilized a computational framework incorporating aqueous solubility, pKa, and coordination bond length.
Employed machine learning models with graph convolution and transfer learning for property prediction.

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Implemented a "computational funnel" to identify suitable ligands for Cesium/Strontium separation.

Main Results:

Successfully identified potential ligands for Cesium/Strontium crystallizing separation.
Machine learning models accurately predicted key chemical characteristics of ligands.
Selected top-ranking ligands that are non-toxic and low-cost for experimental validation.

Conclusions:

The proposed framework significantly enhances the efficiency of ligand discovery for nuclear waste reprocessing.
Computational screening accelerates the identification of promising materials for critical separation processes.
This approach supports the development of sustainable nuclear energy by improving fuel cycle technologies.