Machine-Learning-Guided Ligand Optimization for Americium/Europium Coordination Discrimination

Inorganic Chemistry 2026

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Authors: Dongsheng Yang, Zhiyuan Zhang, Yulong Que, Yan Wu, Tongxin Yu, Shiyi Jiang, Chong Liu
DOI: 10.1021/acs.inorgchem.6c01405
Cited by: 3
References: 38

Abstract

coordination preference relative to literature-reported references. This work demonstrates that coordination-chemistry-informed machine learning enables systematic exploration of ligand chemical space under data-scarce conditions and provides a practical computational screening route for candidate extractants targeting challenging An/Ln discrimination problems.

Fields of Study & Subjects

Machine Learning in Materials Science Asymmetric Hydrogenation and Catalysis Radioactive element chemistry and processing

Publication details

Journal / source
Inorganic Chemistry
Publisher
American Chemical Society (ACS)
Volume
65
Issue
18
Pages
9719–9727
Publication date
30 April 2026
Language
EN