  Accurately predicting material properties is critical for discovering and
designing novel materials. Machine learning technologies have attracted
significant attention in materials science community for their potential for
large-scale screening. Among the machine learning methods, graph convolution
neural networks (GCNNs) have been one of the most successful ones because of
their flexibility and effectiveness in describing 3D structural data. Most
existing GCNN models focus on the topological structure but overly simplify the
three-dimensional geometric structure. In materials science, the 3D-spatial
distribution of the atoms, however, is crucial for determining the atomic
states and interatomic forces. In this paper, we propose an adaptive GCNN with
novel convolutions that model interactions among all neighboring atoms in
three-dimensional space simultaneously. We apply the model to two distinctly
challenging problems on predicting material properties. The first is Henry's
constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is
notoriously difficult because of its high sensitivity to atomic configurations.
The second is the ion conductivity of solid-state crystal materials, which is
difficult because of very few labeled data available for training. The new
model outperforms existing GCNN models on both data sets, suggesting that some
important three-dimensional geometric information is indeed captured by the new
model.
