适应对称的模型用于水的超极化能力
Ryan Elwood-Clarke1, David M Wilkins1
1Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, Belfast BT7 1NN, Northern Ireland, United Kingdom.
机器学习模型可以预测大集群的水的超极化性. 然而,对于散装水,分子张量预测的差异很大,需要针对特定实验改进模型.
科学领域:
- 非线性光学是一种非线性光学.
- 计算化学是一种计算化学.
- 机器学习 机器学习
背景情况:
- 准确建模非线性光学实验需要计算超极化张量.
- 超极化计算在计算上昂贵,因此适合于机器学习方法.
研究的目的:
- 评估最近开发的机器学习模型,用于预测水群的超极化性.
- 在比训练集中的水结构更大,更复杂的水结构上评估模型性能.
主要方法:
- 在小型水 (最多8个分子) 上训练机器学习模型.
- 在更大的集群和散装水配置上测试模型预测.
主要成果:
- 模型成功地预测了具有复杂结构的大群的超极化性.
- 虽然总的超极化性对散装水的描述很好,但单个分子张量预测在模型中显示出显著的差异.
结论:
- 当前的机器学习模型可以准确地模拟取决于总超极化性的实验.
- 对于需要精确分子超极化张量值的实验,需要改进的模型.
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