通过机器学习方法加速NMR屏蔽计算:适用于酸酸盐玻璃
Marco Bertani1,2, Alfonso Pedone1, Francesco Faglioni1
1University of Modena and Reggio Emilia, Department of Chemical and Geological Sciences, via Campi 103, Modena, Italy.
概括
我们开发了一种机器学习模型,用于预测酸盐玻璃中的NMR同otropic磁屏蔽. 这种方法加快了复杂材料的分析,提供了对其结构和性能的洞察.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 固态NMR光谱学 固态NMR光谱学
背景情况:
- 核磁共振 (NMR) 光谱对于材料的表征至关重要.
- 对复杂系统来说,预测NMR参数,如同otropic磁屏蔽 (σiso),是计算密集的.
- 含有和的酸盐玻璃是重要的工业材料.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测 (Mg,Na) 酸盐眼镜中的NMR同位素磁性屏蔽 (σiso).
- 评估培训数据集大小和多样性对ML模型性能和可转移性的影响.
- 为了证明ML模型在模拟大系统的Si神奇角旋转 (MAS) NMR光谱中的实用性.
主要方法:
- 采用了以最小方位支向量回归 (LSSVR) 方法的内核回归 (KRR).
- 机器学习模型使用原子位置的平滑重叠 (SOAP) 描述符进行训练,以表示原子环境.
- 用密度函数理论 (DFT) 计算同位素化学屏蔽值,使用测量器-包含-投影器-增强波 (GIPAW) 方法.
- 训练数据集是使用不同温度和原子间潜力的分子动力学 (MD) 模拟生成的.
主要成果:
- 该ML模型成功预测了NMR同位素磁性屏蔽对17O,23Na,25Mg和29Si原子核.
- 在训练期间广泛探索配置空间显著改善了ML回归器的可转移性.
- 对高达2万个原子的系统进行29个Si MAS NMR光谱的模拟,通过平均数百个MD配置来实现.
- 机器学习方法在传统量子力学计算的计算成本的一小部分提供了结果.
结论:
- 机器学习,特别是KRR与LSSVR,是预测复杂材料NMR参数的强大工具.
- 开发的ML模型能够对大型酸盐玻璃系统的NMR光谱进行高效的模拟和解释.
- 这种方法显著减少了计算时间,使其对材料的表征和发现有价值.
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