Multi-Step Learning and Optimisation

第一年：为CDT的所有一年级学生成员举办的一般培训活动。2-4年级：探索化学搜索空间的有效方法的探索已经从纯粹的计算机技术发展到网络物理技术，指导机器人或人类实验人员。人工智能与物理系统的这种融合正在成为一种成熟的技术，加上高通量化学技术，提供了一种有效实现有效和高效探索的手段，可能超过人类指导的技术。这些方法也可以被调整成为优化化学合成问题的技术，针对某些特征和分子性状。已经开发了闭环系统来设计目标分子的有效生产，并取得了很大的成功。这些技术的一些困难是由于复杂的化学合成任务涉及多个阶段以及必须平衡的过多目标函数。多步建模和设计涉及形成模型链的多个步骤，通常出现在必须生产多种化学产品并随后反应以形成最终产品的情况下。虽然在化学过程合成和合成化学中广泛存在，但存在许多其他类似（和不同）的背景。目前的许多多阶段建模方法严重依赖于专家的判断和猜测，以平衡阶段间和阶段内的目标。这是工艺合成中一个明显的薄弱环节。消除这种形式的猜测可能会导致过程成功的许多措施的显着改善和开发速度的加快。

Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: The quest for effective methods of exploring chemical search spaces has ventured from purely in-silico techniques to cyber-physical ones, guiding either robotic or human experimentalists. This blending of AI with physical systems is becoming a well-established technique, and with the addition of high-throughput chemistry techniques, offers a means to efficiently achieve effective and efficient exploration, potentially exceeding human guided techniques. These methods may also be adapted to become a technique for optimising chemical synthesis problems, targeting certain characteristics and molecular traits. Closed-loop systems have already been developed to design efficient production of target molecules and have found a great deal of success. Some of the difficulties with these techniques come about due to the multiple stages involved in complex chemical synthesis tasks and the plethora of objective functions that must be balanced.Multi-step modelling and design involves multiple steps forming a chain of models, often appearing in situations where multiple chemical products must be produced, and subsequently reacted, to form a final product(s). Although found extensively in chemical process synthesis and synthetic chemistry, many other similar (and dissimilar) contexts exist. Many of the current methods for multistage modelling rely heavily on expert judgement and guesswork to balance objectives, both inter and intra-stage. This represents an obvious weak link in process synthesis. The removal of this form of guesswork is likely to lead to significant improvement in many measures of process success and a speed-up of development.