带隙回归与架构优化的消息传递神经网络
Tim Bechtel1,2, Daniel T Speckhard1,2, Jonathan Godwin3,1
1Humboldt-Universität zu Berlin, Zum Großen Windkanal 2, 12489 Berlin, Germany.
概括
传递信息的神经网络 (MPNNs) 准确地对材料进行分类,并预测非金属的带间隙. 集成的MPNNs为材料科学应用提供了卓越的性能和可靠的不确定性量化.
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:
- 基于图形的神经网络,特别是传递信息的神经网络 (MPNNs),显示出预测固态属性的前景.
- 对材料属性的准确预测对于发现新材料至关重要.
研究的目的:
- 用密度函数理论 (DFT) 数据训练一个MPNN来将材料分类为金属或半导体/绝缘材料.
- 优化MPNN架构和超参数,用于预测非金属材料的带间隙.
- 评估集合方法和不确定性量化,以提高预测准确度.
主要方法:
- 利用了来自AFLOW数据库的密度函数理论 (DFT) 数据.
- 训练了一个传递信息的神经网络 (MPNN) 用于材料分类.
- 执行神经架构搜索,以优化MPNN用于带隙预测.
- 组合了表现最好的模型,并使用蒙特卡洛脱落来量化不确定性.
主要成果:
- MPNN成功地将材料分类为金属或半导体/绝缘材料.
- 一组MPNN在预测带间隙方面显著优于最佳单一模型.
- 组合证明优于蒙特卡洛落的不确定性量化.
结论:
- MPNNs是预测固体电子性质的有效工具.
- 整体建模提高了材料性质的预测准确性和可靠性.
- 该研究分析了该模型在各种材料特性中的适用性领域.
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