Eigencages: Learning a Latent Space of Porous Cage Molecules

14 November 2018, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Porous organic cage molecules harbor nano-sized 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 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 lay. 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 from 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.


Porous Materials
nanoporous materials
porous organic cages
Latent Space
Machine Learning
computational chemistry
singular value decomposition
porous organic cage molecules

Supplementary materials



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