Computation is playing an increasing role in the discovery of materials, including supramolecular materials such as encapsulants. In this work, a function-led computational discovery using an evolutionary algorithm is used to find potential fullerene (C₆₀) encapsulants within the chemical space of porous organic cages. We find that the promising host cages for C₆₀ evolve over the simulations towards systems that share features such as the correct cavity size to host C₆₀, planar tri-topic aldehyde building blocks with a small number of rotational bonds, di-topic amine linkers with functionality on adjacent carbon atoms, high structural symmetry, and strong complex binding affinity towards C₆₀. The proposed cages are chemically feasible and similar to cages already present in the literature, helping to increase the likelihood of the future synthetic realisation of these predictions. The presented approach is generalisable and can be tailored to target a wide range of properties in molecular material systems.

计算在材料发现中发挥着越来越重要的作用，包括超分子材料，如密封剂。在这项工作中，使用进化算法的功能主导的计算发现用于在多孔有机笼的化学空间内寻找潜在的富勒烯 (C _{60 ) 密封剂。}我们发现 C ₆₀的有前途的宿主笼在模拟过程中演变为具有共享特征的系统，例如宿主 C ₆₀的正确腔尺寸、具有少量旋转键的平面三主题醛构建块、双主题胺连接器在相邻碳原子上具有功能性、高结构对称性以及对 C ₆₀的强络合物结合亲和力. 所提出的笼子在化学上是可行的，并且与文献中已经存在的笼子相似，有助于增加未来合成实现这些预测的可能性。所提出的方法是通用的，可以针对分子材料系统中的各种特性进行定制。