对于学习和理解分子轨道的笛卡尔等值表示
Daniel S King1, Daniel Grzenda2, Ray Zhu3
1Department of Chemistry, University of Chicago, Chicago, IL 60637.
概括
我们开发了卡特西亚等价轨道网络 (CEONET),用于在深度学习中表示分子轨道. CEONET准确地预测轨道能量和特征,帮助电子结构理论和活跃空间选择.
科学领域:
- 计算化学计算化学
- 量子化学 是一个量子化学.
- 机器学习 机器学习
背景情况:
- 轨道属性对于理解化学反应和兴奋状态行为至关重要.
- 当前的深度学习模型与几何学和哈密尔顿学相比,缺乏对分子轨道的强有力的表示.
研究的目的:
- 开发和应用高级等价深度学习架构用于分子轨道表示.
- 从计算化学数据对轨道赋予全球标签,包括能量和表征.
主要方法:
- 应用最先进的等价深度学习架构.
- 开发了笛卡尔等价轨道网络 (CEONET) 来表现分子轨道系数.
- 利用基于图形的机器学习,具有等价节点特征.
主要成果:
- CEONET准确地预测了轨道能量和等定量轨道标签.
- CEONET表示为定性轨道特征 (例如,结合/反结合) 提供了一个可解释的潜空间.
- 证明了模型通过轨道推断多引用特征的能力.
结论:
- CEONET提供了一种强大的新表示方式,用于将深度学习与电子结构理论相结合.
- 该网络促进了先进的电子结构方法的自动化和解释.
- CEONET的功能为计算机化学中的机器学习活跃空间选择铺平了道路.
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