基于结构的物理导向双重自我监督学习用于基于结构的材料属性预测
Nihang Fu1, Lai Wei1, Jianjun Hu1
1Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29201, United States.
The journal of physical chemistry letters
|March 5, 2024
概括
本研究介绍了DSSL,这是一种用于材料属性预测的新物理引导自主监督学习 (SSL) 框架. DSSL通过整合生成和对比的SSL策略来提高图形神经网络的性能,改善数据稀缺的预测.
科学领域:
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
- 计算化学计算化学
背景情况:
- 深度学习在物质属性预测方面表现出色,但需要广泛的标记数据.
- 数据稀缺是培训材料科学中的高性能深度学习模型的一个主要限制.
研究的目的:
- 为基于图形神经网络 (GNN) 的材料属性预测开发一个以物理为导向的双重自我监督学习 (SSL) 框架 (DSSL).
- 通过利用SSL技术,应对材料科学中有限的标记数据的挑战.
主要方法:
- DSSL结合了基于节点掩盖的生成SSL和基于原子坐标扰动的对比SSL,以捕获晶体结构信息.
- 使用物理引导的预训练策略,使用原子度预测作为与宏观性质相关的借口任务.
- 在材料项目数据库上预训练了DSSL模型,并在10个不同的材料属性数据集上进行了微调.
主要成果:
- 与基线模型相比,DSSL框架在物质属性预测方面显示出显著的性能改进.
- 实验结果显示,通过结合物理引导的SSL,性能提高了26.89%.
- 该研究验证了将物理原理集成到SSL中的有效性,以加强神经网络训练.
结论:
- 物理引导的双SSL提供了一种强大的方法来克服材料属性预测中的数据限制.
- 通过结合生成和对比策略,DSSL通过有效地从有限的数据中学习来提高GNN的预测准确性.
- 这项工作突出了将领域知识 (物理) 整合到科学发现的AI模型中的潜力.
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