The recently proposed crystal graph convolutional neural network (CGCNN)
offers a highly versatile and accurate machine learning (ML) framework by
learning material properties directly from graph-like representations of
crystal structures ("crystal graphs"). Here, we develop an improved variant of
the CGCNN model (iCGCNN) that outperforms the original by incorporating
information of the Voronoi tessellated crystal structure, explicit 3-body
correlations of neighboring constituent atoms, and an optimized chemical
representation of interatomic bonds in the crystal graphs. We demonstrate the
accuracy of the improved framework in two distinct illustrations: First, when
trained/validated on 180,000/20,000 density functional theory (DFT) calculated
thermodynamic stability entries taken from the Open Quantum Materials Database
(OQMD) and evaluated on a separate test set of 230,000 entries, iCGCNN achieves
a predictive accuracy that is significantly improved, i.e., 20% higher than
that of the original CGCNN. Second, when used to assist high-throughput search
for materials in the ThCr2Si2 structure-type, iCGCNN exhibited a success rate
of 31% which is 310 times higher than an undirected high-throughput search and
2.4 times higher than that of the original CGCNN. Using both CGCNN and iCGCNN,
we screened 132,600 compounds with elemental decorations of the ThCr2Si2
prototype crystal structure and identified a total of 97 new unique stable
compounds by performing 757 DFT calculations, accelerating the computational
time of the high-throughput search by a factor of 130. Our results suggest that
the iCGCNN can be used to accelerate high-throughput discoveries of new
materials by quickly and accurately identifying crystalline compounds with
properties of interest.

本文中提出了一种改进的晶体图卷积神经网络（iCGCNN）来学习晶体结构的材料属性，该方法不仅采用 Voronoi 剖分晶体结构，还考虑了邻近组分原子的 3 体相互作用和晶体图中原子间化学键的优化表示。研究表明，iCGCNN 方法可以用于通过快速准确地识别具有所需性质的晶体化合物来加速高通量新材料发现，从而显著提高预测准确性和成功率。

高效材料发现的改进晶体图卷积神经网络框架

Developing an improved Crystal Graph Convolutional Neural Network  framework for accelerated materials discovery

Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of universal descriptor of materials to predict its various properties.
The limited availability of materials data can be addressed through transfer
learning, while the generic representation was recently addressed by Xie and
Grossman [1], where they developed a crystal graph convolutional neural network
(CGCNN) that provides a unified representation of crystals. In this work, we
develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning
based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN
by simultaneous prediction of various material properties such as Formation
Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774
materials). MT-CGCNN is able to reduce the test error when employed on
correlated properties by upto 8%. The model prediction has lower test error
compared to CGCNN, even when the training data is reduced by 10%. We also
demonstrate our model's better performance through prediction of end user
scenario related to metal/non-metal classification. These results encourage
further development of machine learning approaches which leverage multi-task
learning to address the aforementioned challenges in the discovery of new
materials. We make MT-CGCNN's source code available to encourage reproducible
research.

本研究提出了一种新的模型（MT-CGCNN），通过将 CGCNN 与基于多任务学习的转移学习相结合，成功预测形成能量、带隙和费米能级等多材料性质，取得了较 CGCNN 更好的效果，结果鼓励进一步开发多任务学习的机器学习方法来解决新材料发现中的挑战。