分子晶体在带隙预测中使用神经网络进行图形比较
Takuya Taniguchi1, Mayuko Hosokawa2, Toru Asahi2
1Center for Data Science, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050, Japan.
ACS omega
|October 30, 2023
概括
与材料信息学中的分子图表相比,水晶图改善了材料属性预测. 这项研究表明,晶体图为带隙预测提供了卓越的准确性,大大减少了预测错误.
科学领域:
- 材料 信息学 信息学
- 计算材料科学科学 计算材料科学
- 固态化学 固态化学
背景情况:
- 图形表示对于材料信息学中的材料结构分析至关重要.
- 分子晶体的分子和晶体结构可以用图形表示.
- 缺乏对这些图形表示用于预测建模的比较分析.
研究的目的:
- 为了比较分子图表与晶体图表在预测材料性质方面的有效性.
- 为了评估哪个图形表示产生更高的预测准确性为分子晶体的带隙.
主要方法:
- 利用基于图形的机器学习模型来表示分子晶体.
- 使用分子图表和晶体图表表示方法,对带间隙的预测准确度进行比较.
- 进行定量错误分析以评估晶体结构信息的贡献.
主要成果:
- 结晶图表表示显示,与分子图表表示相比,带间隙的预测准确度更高.
- 错误分析表明,与分子图相比,晶体图减少了预测错误的约0.4倍.
- 观察到晶体结构特征与带隙预测准确度之间存在温和的相关性.
结论:
- 晶体图形表示比分子图形表示更有效,用于分子晶体中带隙预测.
- 结合晶体结构信息显著提高了材料性质预测的准确性.
- 这项研究为在材料信息学中选择最佳图形表示提供了定量基础.
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