核化学与放射化学2023，Vol.45Issue(5)：456-465,10.DOI:10.7538/hhx.2023.45.05.0456
深度学习引导的高通量分子筛选用于锶铯的选择性配位
Deep-Learning-Guided High-Throughput Evaluation of Ligands for Selective Sr/Cs Coordination
摘要
Abstract
From a coordination chemistry perspective,we aimed to advance the knowledge of Sr/Cs separation in the scheme of spent nuclear fuel reprocessing.Based on data mining of crystal structures and deep learning architecture,we summarized and analyzed coordination chemistry properties of Sr/Cs from complex structures(ca.3.3 × 104 samples)of 8 alkaline and alkaline earth elements,especially focusing on coordination bond lengths as a representa-tive figure of merit.Applying a Bayesian optimization approach,we were able to establish a high-performing trans f ormer model which could predict the(differential)coordinative affini-ties toward Sr/Cs of ligand molecules,with exceptional accuracy.As a proof-of-concept,we systematically analyzed ca.9.1 × 103 ligand molecules in terms of potential coordinative selectivity toward Sr/Cs and ranked them.In addition,we also determined different contri-bution of various functional groups for future molecular design of ligands with selectivity.The present study presented fundamental knowledge for coordination chemistry information in the context of radiochemistry and spent nuclear fuel reprocessing,provided guidance and reference for subsequent experiments regarding Sr/Cs separation.关键词
深度学习/贝叶斯优化/乏燃料后处理/Sr/Cs分离Key words
deep learning/Bayesian optimization/spent nuclear fuel reprocessing/Sr/Cs separation分类
核科学引用本文复制引用
张智渊,石伟群,董越,邱雨晴,毕可鑫,胡孔球,戴一阳,周利,刘冲,吉旭..深度学习引导的高通量分子筛选用于锶铯的选择性配位[J].核化学与放射化学,2023,45(5):456-465,10.基金项目
国家自然科学基金资助项目(22176135) （22176135）
