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

试图从配位化学性质差异的角度增进对乏燃料后处理过程中锶铯分离的认识。基于对晶体结构进行数据挖掘和深度学习架构,从8种碱金属和碱土金属元素的配位结构(约3.3×10^(4)个样本)中归纳和分析锶、铯的配位化学性质,尤其是配位键长。通过引入贝叶斯优化工具,建立了高效的transformer模型,可以以很高的准确性预测配体与锶、铯离子分别的结合强度及其差异。作为概念验证,成功对配体分子结构(约9.1×10^(3)个)对锶、铯的潜在配位选择性进行排序,并为未来的配体设计确定了不同官能团对实现配位选择性的贡献度。本研究利用人工智能手段,为乏燃料后处理过程及放射化学语境中元素的配位化学信息及分离技术开发积累基础知识,为后续实验提供指导和参考。

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.3.3×10^(4) samples)of 8 alkaline and alkaline earth elements,especially focusing on coordination bond lengths as a representative figure of merit.Applying a Bayesian optimization approach,we were able to establish a high-performing transformer model which could predict the(differential)coordinative affinities toward Sr/Cs of ligand molecules,with exceptional accuracy.As a proof-of-concept,we systematically analyzed ca.9.1×10^(3 )ligand molecules in terms of potential coordinative selectivity toward Sr/Cs and ranked them.In addition,we also determined different contribution 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.