权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Improving Crystal Structure Prediction via Tailored Force Fields

通过定制力场改进晶体结构预测

基本信息

批准号：
1966646
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1966646
关键词：
Improving Crystal Structure Prediction via

项目摘要

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.

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。现在可以预测具有实际农用化学品或药物利益的分子的晶体能量景观，具有多个柔性扭转和键角。CSP研究现在可以支持决策和风险管理，无论是通过帮助设计旨在结晶特定结构的实验，还是通过提供所有可能的多晶型物已经被识别的再保证。如果有足够精确的模型，这些研究也可以成为预测许多晶体和溶液性质的基础。当前最先进的技术，例如由Claire Claudiman和Costas Pantelides的小组开发的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。虽然这一假设大大扩大了该方法的适用范围，但它往往是不必要的限制。特别地，目标化合物的一种或多种晶体结构可能已经是已知的，或者可以在活性成分开发过程中鉴定。为了充分发挥CSP技术在工业环境中的潜力，重要的是要利用所有这些可用信息。该项目的目的是开发一种CSP方法，可以利用先验知识，以实现更高的准确性和可靠性。使用这些信息来加速搜索多晶型也将进行调查。拟议的项目将建立在帝国理工学院分子系统工程组最近的工作，突出了CSP核心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推导改进的参数值的技术和代码。总的来说，已经发现这导致更准确的晶格能计算。然而，这项工作一直集中在刚性分子，这是简单的比更灵活的分子典型的农用化学品industrial. the拟议项目的主要目标将是制定一个正式的框架，确定力场是专门为特定的化合物的利益。我们提出的方法将联合收割机从头计算CSP技术与先前的固态信息，可用于这些或密切相关的化合物。新方法的有效性将通过对几种工业相关化合物的测试进行评估。