Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Global Attention based Graph Convolutional Neural Networks for Improved
  Materials Property Prediction   Machine learning (ML) methods have gained increasing popularity in exploring
and developing new materials. More specifically, graph neural network (GNN) has
been applied in predicting material properties. In this work, we develop a
novel model, GATGNN, for predicting inorganic material properties based on
graph neural networks composed of multiple graph-attention layers (GAT) and a
global attention layer. Through the application of the GAT layers, our model
can efficiently learn the complex bonds shared among the atoms within each
atom's local neighborhood. Subsequently, the global attention layer provides
the weight coefficients of each atom in the inorganic crystal material which
are used to considerably improve our model's performance. Notably, with the
development of our GATGNN model, we show that our method is able to both
outperform the previous models' predictions and provide insight into the
crystallization of the material.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom