2:10 PM - 2:30 PM
[3Z1-02] A universal 3D voxel descriptor for solid-state materials informatics with convolutional neural networks
Keywords:Materials Infomatics, descriptor for solid systems, CNNs
Materials informatics (MI) is a promising approach to liberate us from the time-consuming trial and error process
for material discoveries. Contrary to molecular systems, however, practical successes of the solid-state MI are very
scarce because existing descriptors insufficiently describe 3D features of eld quantities (e.g., electron distributions
and local potentials). We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in
such a suitable way to implement convolutional neural networks. We examine the reciprocal-lattice 3D voxel
space descriptor encoded from the electron distribution by a regression task with 680 oxides data. The present
scheme outperforms other descriptors in the prediction of Hartree energies that are signicantly relevant to the
long-wavelength distribution of the valence electrons.
for material discoveries. Contrary to molecular systems, however, practical successes of the solid-state MI are very
scarce because existing descriptors insufficiently describe 3D features of eld quantities (e.g., electron distributions
and local potentials). We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in
such a suitable way to implement convolutional neural networks. We examine the reciprocal-lattice 3D voxel
space descriptor encoded from the electron distribution by a regression task with 680 oxides data. The present
scheme outperforms other descriptors in the prediction of Hartree energies that are signicantly relevant to the
long-wavelength distribution of the valence electrons.