MOFormer:用于金属有机框架属性预测的自主监督变压器模型
Zhonglin Cao1, Rishikesh Magar1, Yuyang Wang1
1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States.
Journal of the American Chemical Society
|January 27, 2023
概括
我们开发了一种深度学习模型MOFormer, 这种结构不可知的方法加速了各种应用的最佳MOF的发现.
科学领域:
- 材料科学
- 计算化学
- 机器学习
背景情况:
- 金属有机框架 (MOF) 具有很高的孔隙性,可用于各种应用.
- MOFs的巨大化学空间需要高效的选以进行最佳的材料发现.
- 目前的高通量选方法,如DFT,是计算密集的,需要3D结构.
研究的目的:
- 为加速MOF属性预测提出一种新的结构不可知深度学习方法.
- 在评估假设的MOF时规避3D原子结构的需要.
- 提高特定应用的MOF选的效率和准确性.
主要方法:
- 开发了基于变压器的深度学习模型MOFormer,用于MOF属性预测.
- 使用了结构不可知的方法,将MOF文本字符串表示 (MOFid) 作为输入.
- 引入了一个自主监督的学习框架,用于预训练MOFormer,使用超过400k的公开MOF数据,将结构不可知和基于结构的表示联系起来.
主要成果:
- 与现有的描述器相比,MOFormer实现了最先进的结构不可知预测准确性.
- 预先训练MOFormer模型显著提高了下游任务的预测准确性.
- 与CGCNN等基于结构的模型相比,MOFormer在量子化学性质预测方面表现出更高的数据效率,特别是在有限的数据的情况下.
结论:
- MOFormer为MOF属性预测提供了一种新且高效的深度学习视角.
- 结构不可知的方法加快了发现新MOF的选过程.
- 自主监督的预训提高了MOF属性预测模型的性能和数据效率.
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