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机器学习辅助解释二维固态核磁共振光谱

Wei Tao¹, Wancheng Yu¹, Xiangyu Zou²

¹National Synchrotron Radiation Laboratory, Anhui Provincial Engineering Laboratory of Advanced Functional Polymer Film, CAS Key Laboratory of Soft Matter Chemistry, University of Science and Technology of China, Hefei 230026, China.

Journal of magnetic resonance (San Diego, Calif. : 1997)

|June 11, 2023

概括

一种新的深度神经网络 (DNN) 方法使用固态核磁共振 (SSNMR) 准确地解释复杂的聚合物光谱. 这种机器学习方法有效地确定化学转移异构 (CSA) 张量方向,用于增强的聚合物结构分析.

科学领域:

固态核磁共振 (SSNMR) 光谱学固态核磁共振 (SSNMR) 光谱学
聚合物科学聚合物科学
机器学习应用机器学习应用

背景情况:

多维SSNMR为聚合物提供了关键的结构和动态信息.
解释SSNMR光谱,特别是化学转移异性质 (CSA) 张量方向,是复杂的.
传统的方法,如线性最小正方形匹配,可能耗时且不太准确.

研究的目的:

开发和验证用于解释多维SSNMR数据的深度神经网络 (DNN) 方法.
为了高效准确地确定聚合物中13C和15N的CSA张量定向.
为传统的光谱分析技术提供强大的替代方案.

主要方法:

训练了一个深度神经网络 (DNN) 模型来分析SSNMR分离局域 (SLF) 光谱.
该DNN应用于各种合成和天然聚合物样本.
对定义张量方向的欧勒角的预测准确性进行了评估.

主要成果:

在四个聚合物样本中,DNN方法准确地确定了13C和15N的CSA张量方向.
欧勒角的预测精度在±5°以内.
该方法表现出高效率,培训成本低 (<1秒).

结论:

提出的基于DNN的方法为解释复杂的多维SSNMR光谱提供了一种高效和准确的方法.
这一策略是强大的,并与文献价值观进行了验证.
该方法预计将在分析具有挑战性的聚合物系统方面发挥重要作用.

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