Improving Crystal Structure Prediction via Tailored Force Fields

近年来，在多晶分子和共晶体系晶体结构预测（CSP）的实用方法开发方面取得了巨大进展。现在有可能预测实际农业化学或制药兴趣分子的晶体能量景观，具有多个柔性扭转和键角。CSP研究现在可以通过帮助设计旨在结晶特定结构的实验，或者通过提供所有可能的多态性已经确定的保证，来支持决策制定和风险管理。给定一个足够精确的模型，这样的研究也可以成为预测许多晶体和溶液性质的基础。目前最先进的技术，例如由Claire Adjiman和Costas Pantelides团队开发的CrystalPredictor和CrystalOptimizer代码，通常假设唯一可用的信息是分子结构。虽然这一假设大大扩大了方法的适用性，但它往往是不必要的限制。特别地，在所述化合物的一种或多种晶体结构可以已经已知或可以在活性成分开发过程中被识别。为了在工业环境中充分发挥CSP技术的潜力，重要的是利用所有这些可用信息。该项目的目的是开发一种可以利用先验知识的CSP方法，以达到更高的准确性和可靠性。还将研究利用这些信息来加速对多态的搜索。提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/色散术语的重要性。这导致了利用现有实验结构推导改进参数值的技术和代码。总的来说，这导致了更精确的晶格能量计算。然而，这项工作主要集中在刚性分子上，它们比农用化学品工业中典型的柔性分子更简单。拟议项目的主要目标将是制定一个正式框架，以确定适合感兴趣的特定化合物的力场。我们提出的方法将从头算CSP技术与可用于这些或密切相关化合物的先前固态信息相结合。将通过对几种与工业相关的化合物进行测试来评估新方法的有效性。

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.