通过机器学习的化学转移来确定晶体结构
Martins Balodis1, Manuel Cordova1,2, Albert Hofstetter1
1Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Journal of the American Chemical Society
|April 13, 2022
概括
这项研究引入了一种使用机器学习化学转移来确定固态结构的更快方法. 这种方法加速了药物和其他粉状固体的晶体结构预测.
科学领域:
- 固态化学
- 材料科学
- 计算化学
背景情况:
- 在化学中确定粉状固体的3D原子结构至关重要.
- 固态NMR化学转移对于结构确定是有价值的,但在计算上是昂贵的.
- 目前的方法依赖于昂贵的晶体结构预测和DFT计算.
研究的目的:
- 开发一种计算效率高的固态结构测定方法.
- 克服传统的晶体结构预测和DFT计算的局限性.
- 使得粉状固体的原子层结构分析更快,更容易.
主要方法:
- 使用机器学习生成的同位素化学转移.
- 采用基于蒙特卡洛的结构确定过程.
- 从随机气相构造开始的结构确定.
主要成果:
- 成功确定了安皮西林,皮罗西卡姆和可卡因的晶体结构.
- 确定了药物分子AZD8329的两个多态的晶体结构.
- 与传统方法相比,显著降低了计算成本.
结论:
- 机器学习的化学转移可以有效地指导固态结构的确定.
- 开发的方法为晶体结构预测提供了更快的替代方案.
- 这种方法在化学,材料科学和制药研究中具有广泛的应用.
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