德尔菲 (Delfi):一个计算机神话,用于为激发状态计算推密度函数
Davide Avagliano1,2, Marta Skreta2,3, Sebastian Arellano-Rubach4
1Department of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada davide.avagliano@utoronto.ca alan@aspuru.com.
我们开发了DELFI,这是一个神经网络工具,它建议密度函数理论 (DFT) 函数用于时间依赖的DFT (TD-DFT) 计算. 德尔菲简化了为准确的分子光物理性质预测选择正确的功能.
科学领域:
- 计算量子化学是一种量子化学.
- 材料科学是一种材料科学.
- 机器学习是机器学习.
背景情况:
- 密度函数理论 (DFT) 在计算化学中至关重要,但为激发状态计算 (TD-DFT) 选择合适的函数是具有挑战性的.
- 准确预测分子光物理性质需要仔细考虑多个电子状态和过渡.
研究的目的:
- 开发一个自动化系统,为TD-DFT计算推DFT函数.
- 为了简化用于预测分子光学性质的函数的选择.
主要方法:
- 开发了一个评分系统,以评估DFT函数的精度与激发状态的高精度参考.
- 创建了一个包含400万个数据点的大数据库,评估有机分子的常见功能.
- 在这些数据上训练了一个注意力神经网络 (DELFI) 图表,以预测功能性能.
主要成果:
- 德尔菲为TD-DFT计算提供了准确的功能建议,将属性预测转化为回归任务.
- 该系统通过选择螺旋 - 墨氨酸异构体的功能性和选5万多个有机光伏分子来验证.
- 发布了一个开放的数据库和Web应用程序,以促进社区使用.
结论:
- 德尔菲显著减轻了为TD-DFT选择DFT函数的困难.
- 数据驱动的方法可以快速选和准确预测大型分子数据集的光学特性.
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