Advanced Crystal Shape Descriptors for Precision Particulate Design, Characterisation and Processing (Shape4PPD)

发展和提高我们的研发和制造能力，以制备更多更高质量的晶体材料，已成为一种日益增长的社会需求，从而也成为工业需求。这就要求在整个研发周期内提高精度和速度，以满足精细化工行业对精密晶体不断发展的需求，如制药、农用化学品和添加剂。例如，预计将以更快的分子到患者的旅程、更小的体积和更低的成本生产更具差异化的产品系列。在药品方面，这将提供更广泛的更有针对性的药物和剂型，确保提供安全性和有效性大大提高的患者靶向剂型，从而极大地造福于经济、环境和社会。晶体产品多样性的增加要求实施数字使能和人工智能技术，正如英国政府政策和全球倡议所强调的那样。晶体的表面特性对于通过溶液结晶进行数字设计和制造精密颗粒非常重要。对结晶颗粒表面的控制是精细化学工业的一个重要目标，精细化学工业生产约70%的成分为固体(结晶)形式。这些晶体有其独特的形状和表面化学，当变化时，可能会对产品质量和性能产生不利影响。具体地说，此类产品的有效数字化设计及其制造过程需要详细了解产品配方成分的表面属性。目前，在将分子和单晶水平上的可测量性质与同一材料在制造或以颗粒形式使用时的行为和性能联系起来时，存在着一个关键差距。这种前景要求开发一种数字平台，能够表征、监控和控制晶体的大小和形状。然而，目前商用颗粒测量系统提供的现有晶体形状描述符能力有限，相应的算法往往不切实际地基于非球形晶体可以被视为球形晶体的假设。因此，开发先进的受流程启发的分析工具，特别是基于人工智能的方法，结合第一原理、基于形状的模型显然是必要的。为了确保英国以研究为主导的精细化工和制药行业继续在产品开发和制造方面提供杰出的国际领先地位，从而保持和提高其全球竞争力，这些方法是重要的。拟议的研究将应用基于晶体形态预测(正向工程)的机器学习，从2D过程中的显微镜数据映射回对晶体3D形状的描述(逆向工程)，并通过此映射到其功能表面属性。这将有助于设计和控制更高效、更灵活的结晶精细化学品制造工艺，提供尺寸和形状比目前可行的更严格的精密晶体，从而使产品具有更高的一致性、更少的变异性和更高的质量。该成果将为精密颗粒制造的晶体形状表征和过程动力学控制提供一个数字平台。开发的方法将通过学术合作(如CMAC中心、INFORM2020、剑桥晶体数据中心、帝国理工学院等)共享。以及工业(英飞凌、Keyence、辉瑞、罗氏、先正达等)并在适当的时候扩大到更广泛的范围，预计可能会产生巨大的经济和社会影响。

Developing and improving our R&D and manufacturing capabilities to prepare greater numbers of higher quality crystalline materials has become a growing societal and hence industrial need. This requires higher levels of precision and speed throughout the R&D development cycle to meet the evolving needs for precision crystals in fine chemical's sector such as for pharmaceuticals, agrochemicals and additives. For example, a more differentiated product range is expected to be produced with a significantly faster molecule to patient journey, in much smaller volumes and at significantly lower costs. For pharmaceuticals, this will provide a wider range of more targeted medicines and dosage forms, ensuring the delivery of patient-targeted dosage forms with much improved safety and efficacy, hence enormously benefiting economy, environment and society. Such an increase in the multiplicity of crystalline products demands the implementation of digitally-enabled and AI technologies as highlighted in UK government policy and global initiatives.The surface properties of crystals are very important for the digital design and manufacture of precision particles via solution crystallisation. Control of the surfaces expressed on crystalline particles represents a critical objective for the fine chemical industry which manufactures ca. 70% of their ingredients in solid (crystalline) form. These crystals have their unique shapes and surface chemistry which, when variable, can impact adversely upon product quality and performance. Specifically, the effective digital design of such products and the associated processes for their manufacture demands a detailed knowledge of surface properties of the product's formulation ingredients. Currently there exists a critical gap to relate the measurable properties at the molecular and single crystal levels to the behaviour and performance of the same material when it is manufactured or used in particulate form. This perspective demands the development of a digitally-enabled platform which is able to characterise, monitor and control crystal size and shape. However, existing crystal shape descriptors available with current commercial particle measurement systems have limited capabilities and the corresponding algorithms tend, unrealistically, to be based upon the assumption that non-spherical crystals can be treated as spherical ones. Therefore, the development of advanced process-inspired analytical tools, particularly of AI-based approach, combining with first-principle, shape-based models are clearly needed. Such approaches are important in order to ensure that the UK's research-led fine chemical and pharmaceutical industry continues to provide outstanding international leadership in product development and manufacture so maintaining and enhancing its global competitiveness.The proposed research will apply machine learning based upon crystal morphology prediction (forward engineering) to map from 2D in-process microscopy data back to a description of a crystal's 3D shape (reverse engineering) and, through this, to its functional surface properties. This will enable the design and control of more efficient and agile manufacturing processes for crystalline fine chemicals, delivering precision crystals with a much tighter specification in terms of their size and shape than is currently feasible, hence resulting in products having more consistency, less variability, higher quality. The outcomes will be a digital platform of crystal shape characterisation and process dynamics control for precision particle manufacture. The approach developed will be shared through academic collaboration (such as the CMAC Hub, INFORM2020, Cambridge Crystallographic Data Centre, Imperial College etc.) and with industry (Infineum, Keyence, Pfizer, Roche, Syngenta etc.) and also extended in due course more widely, expecting potentially enormous economic and societal impact.