Predicting Organic Crystal Structures with Quantum Chemistry

加州大学河滨分校的Gregory Beran获得了化学理论、模型和计算方法项目的奖励，他开发了一种计算方法，可以有效、可靠地预测分子晶体结构和性质。该模型将单个分子的量子力学处理及其主要相互作用与经典处理相结合，而较弱的相互作用则使用计算成本低廉的经典极化力场进行近似。该项目有三个主要目标：将模型发展成为实用的计算工具，在一组经过充分研究的分子晶体上对模型的性能进行基准测试，并将该模型应用于几个具有挑战性的晶体结构预测问题。这项研究将有助于在进行任何实验之前预测分子如何在固态晶体中聚集在一起。晶体填塞会影响一些有用的特性，如药物在体内的溶解度、有机半导体材料的载电荷性能、高能材料的爆轰以及固态化学反应的效率。这项研究将为控制晶体堆积的物理相互作用以及如何控制这些相互作用来设计新的高性能分子材料提供新的见解。

Gregory Beran of the University of California at Riverside is supported by an award from the Chemical Theory, Models and Computational Methods program to develop a computational method that enables the efficient and reliable prediction of molecular crystal structures and properties. The model will combine a quantum mechanical treatment of individual molecules and their dominant interactions with a classical treatment, while weaker interactions are being approximated using a computationally inexpensive classical polarizable force field. This project has three principle objectives: to develop the model into a practical computational tool, to benchmark the performance of the model on a set of well-studied molecular crystals, and to apply this model to several challenging crystal structure prediction problems. This research will help make it possible to predict, before any experiments are performed, how molecules pack together in solid-state crystals. Crystal packing affects useful properties such as the solubility of pharmaceuticals in the body, the charge-carrier performance of organic semi-conductor materials, the detonation of energetic materials, and efficiency of solid-state chemical reactions. The research will provide new insights into the physical interactions that govern crystal packing and into how these can be controlled to design new high-performance molecular materials.