Eigencages: Learning a Latent Space of Porous Cage Molecules

<div>Porous organic cage molecules harbor nano-sized cavities that can selectively adsorb gas molecules, lending them applications in separations and sensing. The geometry of</div><div>the cavity strongly influences their adsorptive selectivity. </div><div><br></div><div>For comparing cages and predicting their adsorption properties, we embed/encode a set of 74 porous organic</div><div>cage molecules into a low-dimensional, latent “cage space” on the basis of their intrinsic porosity. </div><div><br></div><div>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. </div><div><br></div><div>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.</div>