Porous organic cage molecules harbor nanosized cavities that can selectively adsorb gas molecules, lending them applications in separations and sensing. The geometry of the cavity strongly influences their adsorptive selectivity. For comparing cages and predicting their adsorption properties, we embed/encode a set of 74 porous organic cage molecules into a low-dimensional, latent “cage space” on the basis of their intrinsic porosity. We first computationally scan each cage to generate a three-dimensional (3D) image of its porosity. Leveraging the singular value decomposition, in an unsupervised manner, we then learn across all cages an approximate, lower-dimensional subspace in which the 3D porosity images congregate. The “eigencages” are the set of orthogonal, characteristic 3D porosity images that span this lower-dimensional subspace, ordered in terms of importance. A latent representation/encoding of each cage follows by approximately expressing it as a combination of the eigencages. We show that the learned encoding captures salient features of the cavities of porous cages and is predictive of properties of the cages that arise from cavity shape. Our methods could be applied to learn latent representations of cavities within other classes of porous materials and of shapes of molecules in general.

中文翻译：

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本征笼：了解多孔笼分子的潜在空间

多孔的有机笼状分子具有纳米级的空腔，可以选择性地吸附气体分子，从而使其可用于分离和传感。空腔的几何形状强烈影响其吸附选择性。为了比较笼子并预测其吸附性能，我们基于其固有的孔隙率将74个多孔有机笼子分子嵌入/编码到低维，潜在的“笼子空间”中。我们首先以计算方式扫描每个笼子，以生成其孔隙率的三维（3D）图像。利用非监督方式的奇异值分解，我们然后在所有笼子中学习一个近似的低维子空间，在该子空间中3D孔隙率图像会聚在其中。“本征笼”是一组跨此低维子空间的正交特征3D孔隙度图像集，重要性排序。每个笼子的潜在表示/编码之后，将其近似表示为特征笼子的组合。我们表明，学习的编码捕获多孔笼的腔的显着特征，并预测由腔形状引起的笼的特性。我们的方法可用于学习其他类别的多孔材料中的腔体以及分子的总体形状的潜在表示。