Accurate molecular crystal modeling for nuclear magnetic resonance crystallography

滨江的加州大学的Gregory Beran得到了化学系化学理论、模型和计算方法（CTMC）项目以及计算和数据支持科学与工程项目（CDS& E）的支持，以开发最先进的计算策略，用于预测最稳定的分子晶体结构，并将这些预测与实验相结合，以确定晶体堆积，药物、新型有机电子材料和许多其他结晶固体的功效关键取决于分子聚集在一起形成晶体的方式。 从长远来看，这些药物晶体包装的研究将有助于提高药物的保质期，溶解度和其他重要性能。 该项目产生的软件将广泛提供给社区。 首席研究员还从事推广活动，使广大学生和公众认识到科学计算，以实现这些目标，新的电子结构算法，提供更准确的描述分子间的相互作用，以较低的计算成本正在开发，这将使可靠的晶体结构建模。 预测核磁共振化学位移的改进技术正在开发中，以允许实验测量的光谱和三维结构模型之间的映射。 需要高质量的化学位移预测来区分不同的晶体堆积形式。 这些技术正在被整合到一个工作流程中，该工作流程将晶体结构预测、核磁共振光谱模拟和实验测量相结合，以确定两种重要药物的几种未知晶体形式的结构。 这些新方法被整合到软件中，这些软件将根据开源许可条款发布。 该代码将被集成到Q-CHEM软件包中，这是一个具有非常大用户群的商业代码。

Gregory Beran at the University of California, Riverside is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division and the Computational and Data-Enabled Science and Engineering Program (CDS&E) to develop state-of-the-art computational strategies for predicting the most stable molecular crystal structures and for coupling these predictions with experiments to determine crystal packing, The efficacy of pharmaceuticals, novel organic electronic materials, and many other crystalline solids depends critically on the manner in which the molecules pack together to form crystals. In the longer term, these studies of pharmaceutical crystal packing will help the formulation of medicinal drugs with improved shelf-life, solubility, and other important properties. Software resulting from this project will be made widely available to the community. The principal investigator also engages in outreach activities to bring awareness of scientific computing to the a broad range of students and to the general public.To achieve these goals, new electronic structure algorithms that provide more accurate descriptions of intermolecular interactions at lower computational cost are being developed that will enable reliable crystal structure modeling. Improved techniques for predicting nuclear magnetic resonance chemical shifts are being developed to allow mapping between experimentally measured spectra and three-dimensional structural models. High-quality chemical shift predictions are needed to discriminate among different crystal packing forms. These techniques are being integrated into a workflow that combines crystal structure prediction, nuclear magnetic resonance spectrum simulations, and experimental measurements to identify the structures for several unknown crystal forms of two important pharmaceuticals. The new methods are incorporated into software which will be released to under open-source licensing terms. The code will be integrated into the Q-CHEM package, a commercial code with a very large user base.