Jorge Aguilera-Iparraguirre

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We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are(More)
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled(More)
We have computed the atomization energies of nineteen C 3 H x molecules and radicals using explicitly-correlated coupled-cluster theory including corrections for core–core and core–valence correlation, scalar and spin–orbit relativistic effects, and anharmonic vibra-tional zero-point energies. Equilibrium geometries were obtained at the coupled-cluster(More)
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