用机器学习预测超铀元素配位剂性能，解决实验难、计算贵问题。
Data-driven approach to the design of complexing agents for trivalent transuranium elements
- 构建新型神经网络模型，利用已有实验数据提升预测精度。
- 识别影响配合物稳定性的关键分子片段，指导配位剂设计。
- 适用于稀有高成本元素研究，适合化学与材料领域学者参考。
超铀元素配合物的性质长期受到化学多个领域的关注。然而，其实验研究受限于元素稀少、成本高昂及特殊操作条件，而量子化学计算对大体系又过于复杂。为克服上述挑战，我们采用现代机器学习方法，构建了一种新型神经网络架构，利用现有实验数据显著提升模型质量。同时，我们描述了该模型的应用范围，并识别出对配合物稳定性影响最大的分子片段。
原文摘要 · Abstract (English)
The properties of complexes with transuranium elements have long been the object of research in various fields of chemistry. However, their experimental study is complicated by their rarity, high cost and special conditions necessary for working with such elements, and the complexity of quantum chemical calculations does not allow their use for large systems. To overcome these problems, we used modern machine learning methods to create a novel neural network architecture that allows to use available experimental data on a number of elements and thus significantly improve the quality of the resulting models. We also described the applicability domain of the presented model and identified the molecular fragments that most influence the stability of the complexes.
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