Characterisation of Crystalline Materials through Imaging, Image Processing and Machine Learning for 3D Shape Description

有机材料的晶体生长在特种和精细化学工业中具有重要意义。这反映了它在材料纯化方面的实用性，以及它在制备各种化合物方面的用途，这些化合物具有良好定义的晶体尺寸、形状和多晶形式，以获得最佳产品性能。后者是重要的，例如，在确保可重复的溶解和稳定性行为需要维持配方产品中的成分的安全性和有效性。有机精细化学品固有的复杂性直接影响其物理化学颗粒性能，特别是其低对称性晶体结构的结晶，具有各向异性的形态和表面性能。这些特性的变化或可变性会影响材料的下游性能，例如生物利用度、粉末处理、稳定性和可制造性。已知形成固溶体的杂质会影响形貌（形状和表面性质）。目前的颗粒尺寸测量在形状表征方面可能过于简单，主要集中在球形颗粒上。这些方法不能反映精细化学品中重要的晶体形态，因为不同的晶体表面可能具有不同的表面化学性质，因此与加工环境的分子间相互作用也不同。目前，在能够将分子结构与颗粒形式的相同材料的性能联系起来的能力方面存在关键差距。这种知识差距导致人们对融合分子晶体学数据、模拟性质和人工智能（AI）方法的兴趣越来越大。该项目的目的是通过应用数字人工智能技术来开发基于形态学的形状描述符，以在3D中精确表征晶体颗粒，从而满足上述需求。为了做到这一点，机器学习将被应用于将过程中的显微镜图像映射到3D晶体形状和功能特性的描述。该项目将有助于有机晶体材料的设计，使其具有更紧密的粒度/形状规格，具有更高的一致性和更少的可变性。该项目将探索工艺杂质的影响，并旨在模拟固溶体对晶体生长和表面性能的影响。该学生将把结晶技术与人工智能/机器学习结合起来，进行结晶工艺工程，重点是3D晶体表征。项目目标包括1。深入的文献综述2。用于晶体形态和生长动态表征的新型原位细胞的开发和调试收集不同工艺条件下制备的晶体图像进行晶体形态表征和生长动力学的实验研究使用分子和晶体建模来表征原位显微镜数据，并将其与人工智能/机器学习技术相结合。扩展在线成像的方法，用于在批处理过程中监测晶体种群生长的动态。该项目以粒子技术和人工智能/机器学习领域为中心，由这两个领域的监督团队中的专家组成。该项目将利用利兹大学的结晶实验室进行密集实验，这些实验室配备了原位显微镜、结晶系统（1mL至20L）、固态表征（Keyence数码显微镜、IGC/GC、DSC、TGA、FTIR、UV-vis、Morphologi G3等）。在项目的工业合作伙伴先正达的支持下，将为工业案例研究提供实习机会。

The crystal growth of organic materials is of significant importance within the speciality and fine chemical industries. This reflects its utility in materials purification and its use in preparing a wide range of compounds which have the well-defined crystal size, shape and polymorphic form needed for optimal product performance. The latter is important e.g., in ensuring the reproducible dissolution and stability behaviour needed to maintain the safety and efficacy of ingredients within formulated products. The inherent complexity of organic fine chemicals directly impacts on their physical chemical particulate properties, notably their crystallisation in low symmetry crystallographic structures with anisotropic morphologies and surface properties. Changes to, or variability in, these properties can affect downstream performance of the material, e.g., bioavailability, powder handling, stability and manufacturability. Impurities, that form solid solutions are known to influence morphology (shape as well as surface properties). Current particle sizing measurements can be over-simplistic in terms of shape characterisation being focussed mostly on spherical particles. Such methods do not reflect crystal morphologies important in fine chemicals where different crystal faces can have different surface chemistry and hence different intermolecular interactions with their processing environments. Currently, there is a critical gap in capability to be able to relate molecular structure to performance of the same material in particulate form. This knowledge gap has led to increasing interest in fusing molecular crystallographic data, simulated properties and artificial intelligence (AI) approaches. The aim of this project is to address the above need by applying digital AI-enabled technology to develop morphologically-based shape descriptors for precisely characterising crystalline particulates in 3D. To do this machine learning will be applied to map the images from in-process microscopy to a description of 3D crystal shape and functional properties. The project will help enable the design of organic crystalline materials to a much tighter particle size/shape specification with more consistency and less variability. The project will explore the impact of process impurities and aim to model the impact of solid solutions on crystal growth and surface properties. The student will integrate the crystallisation technology with AI/machine learning for crystallisation process engineering with a focus on 3D crystal characterisation. The project aims encompass 1. Intensive literature review 2. Development and commissioning of a new in-situ cell for the dynamic characterisation of crystal morphology and growth 3. Experimental studies of the morphological characterisation and crystal growth kinetics collecting crystal images prepared under varying process conditions 4. Using molecular and crystallographic modelling to characterise in-situ microscopy data integrating this with AI/machine learning techniques 5. Extending approaches to online imaging for monitoring the dynamics of the growth of a population of crystals during a batch processing. The project is centred around the areas of particle technology and AI/machine learning with experts within the supervisory team in both areas. This project will be experimentally intensive with making use of the crystallisation laboratories at Leeds which are well equipped with e.g., in-situ microscopes, crystallisation systems (1mL to 20L), solid-state characterisation (Keyence Digital Microscope, IGC/GC, DSC, TGA, FTIR, UV-vis, Morphologi G3 etc). Support by the project's industrial partner, Syngenta, will provide placement opportunities for industrial case studies.