Improving Crystal Structure Prediction via Tailored Force Fields

近年来，在多晶型分子和共晶体系的晶体结构预测(CSP)的实用方法的发展方面取得了巨大的进展。现在有可能预测具有实际农业化学或制药兴趣的分子的晶体能量图景，具有多个灵活的扭曲和键角。CSP研究现在可以支持决策和风险管理，要么通过帮助设计旨在明确特定结构的实验，要么通过提供重新保证所有可能的多态已经被识别。给出一个足够精确的模型，这样的研究也可以形成许多晶体和溶液性质预测的基础。目前最先进的技术，例如由Claire Adjiman和Costas Panteldes小组开发的CrystalPredictor和CrystalOptimizer代码中包含的那些技术，通常假设唯一可用的信息是分子结构。尽管这一假设极大地扩大了该方法的适用范围，但它往往具有不必要的限制性。具体地说，在活性成分开发过程中，可能已经知道或鉴定了目标化合物的一个或多个晶体结构。为了充分实现CSP技术在工业环境中的潜力，利用所有这些可用的信息是很重要的。该项目的目的是开发一种可利用先验知识的CSP方法，以实现更高的准确性和可靠性。还将调查利用这些信息加速多态搜索的情况。拟议的项目将建立在帝国理工学院分子系统工程小组最近的工作基础上，该小组强调了在CSP核心的许多模型中使用的经验排斥/分散项的重要性。这导致了使用可用的实验结构来推导改进的参数值的技术和代码。总体而言，这已被发现导致更准确的晶格能量计算。然而，这项工作主要集中在刚性分子上，这种分子比农用化学品行业中典型的更灵活的分子更简单。拟议项目的主要目标将是开发一个正式的框架，用于确定针对特定感兴趣化合物量身定做的力场。我们建议的方法将把从头算CSP技术与这些或密切相关化合物可用的先前固态信息结合起来。新方法的有效性将通过对几种与工业相关的化合物进行测试来评估。

There has been tremendous progress in recent years in the development of practical approaches to crystal structure prediction (CSP) for polymorphic molecules and co-crystal systems. It is now possible to predict the crystal energy landscape of molecules of practical agrochemical or pharmaceutical interest, with multiple flexible torsions and bond angles. CSP studies can now support decision making and risk management, either by helping to design experiments aimed at crystallising a specific structure or by offering re-assurance that all likely polymorphs have already been identified. Given a sufficiently accurate model, such studies can also form the basis for the prediction of numerous crystal and solution properties. Current state-of-the-art techniques, such as those incorporated in the CrystalPredictor and CrystalOptimizer codes developed by the groups of Claire Adjiman and Costas Pantelides, generally assume that the only available information is the molecular structure. Although this assumption significantly widens the applicability of the approach, it is often unnecessarily restrictive. In particular, one or more crystal structures for the compound of interest may already be already known or may be identified during the course of active ingredient development. To fully realise the potential of CSP techniques within an industrial context, it is important to make use of all such available information. The aim of this project is to develop a CSP methodology that can take advantage of prior knowledge in order to achieve greater accuracy and reliability. The use of such information to accelerate the search for polymorphs will also be investigated. The proposed project will build on recent work within the Molecular Systems Engineering group at Imperial College that has highlighted the importance of the empirical repulsion/dispersion terms used in many of the models at the heart of CSP. This has led to techniques and codes for the derivation of improved parameter values using available experimental structures. Overall, this has been found to lead to much more accurate lattice energy calculations. However, this work has been focused on rigid molecules, which are simpler than the more flexible molecules typical of the agrochemicals industry.The primary objective of the proposed project will be develop a formal framework for determining force fields that are tailored to specific compounds of interest. Our proposed approach will combine ab initio CSP techniques with prior solid state information that is available for these or closely-related compounds. The effectiveness of the new methodology will be assessed via testing on several compounds of industrial relevance.