Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as “easy-to-synthesize” or “difficult-to-synthesize” by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

中文翻译：

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材料前体评分：模拟化学家对多孔有机笼前体合成可及性的直觉

越来越多地使用计算来加速新材料的发现。一个具体的例子是多孔分子材料，特别是多孔有机笼，其中材料的孔隙率主要来自分子本身的内腔。具有有用特性的新结构的计算发现目前受到从计算预测过渡到合成实现的困难的阻碍。实验验证的尝试通常既耗时又昂贵，而且常常是材料发现的关键瓶颈。在这项工作中，我们开发了一种多孔分子的计算筛选工作流程，其中包括考虑材料前体的合成难度，旨在简化计算预测和实验实现之间的过渡。我们通过首先收集 12,553 个分子的数据来训练机器学习模型，这些分子被具有多年有机合成经验的专家化学家归类为“易于合成”或“难以合成”。我们使用了一种方法来解决我们数据集中存在的类别不平衡问题，产生了一个二元分类器，能够对易于合成的分子进行分类，并且几乎没有误报。然后，我们在多孔有机分子的计算筛选过程中使用我们的模型来偏向于更容易合成的前体，这将使它们成为实验实现和材料开发的有希望的候选者。我们发现，即使将前体限制为更容易合成的那些，