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BNM-CDGNN:批量规范化多层感知子晶体距离图神经网络,用于优异性能的晶体属性预测

Kong Meng¹, Chenyu Huang¹, Yaxin Wang¹

¹Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China.

Journal of chemical information and modeling

|September 18, 2023

概括

一个新的图形神经网络 (GNN) 模型,BNM-CDGNN,通过在聚合后有效处理隐藏层来提高晶体属性预测的准确性. 这种方法增强了几何特征的学习,以便更好地预测材料属性.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
机器学习机器学习

背景情况:

图形神经网络 (GNN) 通过捕捉结构特征,擅长预测晶体特性.
现有的GNN模型在隐藏层后的聚合中扎着性能限制.
准确的晶体属性预测对于材料的发现和开发至关重要.

研究的目的:

引入一种新的GNN模型,BNM-CDGNN,旨在克服现有的GNN架构对晶体属性预测的局限性.
提高模型学习精确几何信息的能力,提高训练性能.

主要方法:

提出了批量规范化多层感知晶距离图神经网络 (BNM-CDGNN).
使用原子距离向量编码的晶体几何.
采用辐射基的功能作为一个注意力罩在图形卷积层的旋转不变性.
在多个隐藏层中集成平均聚合和批量正常化.

主要成果:

BNM-CDGNN在晶体属性预测方面表现出显著提高的准确性.
该模型有效地捕获了几何信息和旋转不变.
在聚合后更好地处理隐藏层,有助于提高训练性能.

结论:

与SchNet和MPNN等既有模型相比,BNM-CDGNN为晶体属性预测提供了一种优越的方法.
这种新的架构有效地利用了几何特征,并改善了模型训练.
这一进步有望通过准确的属性预测加速材料科学研究.

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