机器学习的27个Al NMR电场梯度张量对于来自DFT的晶体结构
He Sun1, Shyam Dwaraknath2, Handong Ling3
1Department of Chemistry, Washington University, St. Louis, MO, 63130, USA.
一个新的机器学习模型快速预测-27 (27Al) 晶体结构的电场梯度 (EFG) 参数. 这通过减少对昂贵计算的依赖,加速了NMR结晶学中的结构改进.
科学领域:
- 固态化学 固态化学
- 晶体学 晶体学是指结晶学.
- 计算化学是一种计算化学.
背景情况:
- 核磁共振晶体学通过使用固态核磁共振和DFT来改进原子坐标.
- 预测27Al等四极核的电场梯度 (EFG) 张量是计算上昂贵的.
- 目前的方法限制了对定义清楚的结构的适用性.
研究的目的:
- 开发一个快速,低成本的机器学习模型来预测EFG参数.
- 能够为含有27Al.Al的晶体固体提供更快,更容易获得的结构提炼.
- 提高NMR晶体学中的结构赋值的准确性.
主要方法:
- 使用DFT对1681 27Al晶体固体进行计算的8081 EFG张力器.
- 开发了一个随机森林机器学习模型,使用本地结构动机和元素参数.
- 对105个实验测量27Al位点进行基准模型预测.
主要成果:
- 机器学习模型实现了高精度,R2 = 0.98和RMSE = 0.61MHz为四极合常量 (CQ).
- 简单的局部几何特征被发现是主要的预测因素.
- 该模型显示了预先精制结构赋值的巨大潜力.
结论:
- 已经开发了一个快速而准确的ML模型来预测27Al EFG参数.
- 这种工具可以显著降低与NMR结晶学相关的计算成本.
- 该模型有助于结构赋值和精细化,帮助研究人员分析复杂的NMR光谱.
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