Eigencages: Learning a Latent Space of Porous Cage Molecules.

ABSTRACT: 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 i

SUBMITTER: Sturluson A 

PROVIDER: S-EPMC6311689 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature