检查晶体结构的图形神经网络:捕获周期性的局限性和机会
Sheng Gong1, Keqiang Yan2, Tian Xie3
1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Science advances
|November 10, 2023
概括
图形神经网络 (GNN) 在与晶体结构周期性作斗争. 将GNN与人为设计的描述符混合,可以提高材料特性如声子能量和热容量的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 图形神经网络 (GNN) 越来越多地用于学习晶体结构表示.
- 缺乏对捕获晶体结构的GNN限制的系统评估.
- 了解这些局限性对于推进数据驱动材料发现至关重要.
研究的目的:
- 系统地评估GNN在表示晶体结构方面的局限性.
- 通过人类设计的描述器来研究GNN理解晶体结构周期性的能力.
- 提出一种方法来提高GNN性能,用于预测材料特性.
主要方法:
- 利用人类设计的描述符作为知识汇编来探讨GNN的能力.
- 通过检查局部表达力,远程信息处理和读取函数来分析捕获晶体周期性的GNN失败.
- 提出了一种混合化方法,将描述符与GNN结合起来.
主要成果:
- 当前最先进的GNN在准确捕捉晶体结构周期性方面存在局限性.
- 在GNN的本地,远程和读取功能方面分析了故障.
- 拟议的描述符-GNN杂交直接补充了缺少的信息,提高了预测准确性.
结论:
- GNN需要增强才能充分捕捉晶体结构的细微差别,特别是周期性.
- 将GNN与人类设计的描述符混合,提供了一个通用和有效的解决方案.
- 这种方法显著改善了对材料属性的预测,例如声子的内部能量和热容量.
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