3 citations · 5 across the 4 of their papers we have counts for
4 papers
Point Group Equivariant Graph Neural Networks for Materials
Alexander J. Heilman, Qimin Yan
Equivariant graph neural networks have proven effective tools for inference of material's properties directly from their structure. Traditionally, these have been applied such that…
Crystal Hypergraph Convolutional Networks
Alexander J. Heilman, Weiyi Gong, Qimin Yan
Graph representations of solid state materials that encode only interatomic distance lack geometrical resolution, resulting in degenerate representations that may map distinct stru…
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of Crystals
Alex Heilman, Claire Schlesinger, Qimin Yan
Modern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, a…
Finite-Function-Encoding Quantum States
Paul Appel, Alexander J. Heilman, Ezekiel W. Wertz +4
We introduce finite-function-encoding (FFE) states which encode arbitrary -valued logic functions, i.e., multivariate functions over the ring of integers modulo , and investi…