Chemistry INformEd MAchine learning in emulsion polymerization processes and products

机器学习(ML)系统继续给日常生活的许多方面带来革命性的变化，但尽管它们具有巨大的潜力，但尚未在聚合物科学中产生重大影响。阻碍机器学习在聚合物科学和许多其他物理科学中更广泛使用的一个主要问题在于，在收集足够的数据以有效地训练机器学习模型方面存在挑战。这本身并不一定是问题，是机器学习领域经常遇到的问题，但只有彻底了解兴趣问题背后的科学才能解决。CINEMA的目标是提供一个培训平台，通过将多年研究中获得的基础知识纳入机器学习系统的培训，使下一代聚合物科学家能够将聚合物科学带入21世纪。这种知识驱动的机器学习方法将电影的科学问题放在了使用机器学习解决基础科学问题的前沿，也为下一代科学家提供了完美的培训平台，对他们来说，人工智能的使用将是一个无价的工具。

Machine learning (ML) systems continue to revolutionize many aspects of daily life, but despite their immense potential have yet to impact significantly in polymer science. One major issue that is hindering the more widespread use of machine learning in polymer science, and many other physical sciences, liesin the challenges in amassing sufficient data to efficiently train machine learning models. This in itself is not necessarily a problem, and is an issue frequently encountered in the machine learning field, but can only be resolved by a thorough understanding of the science behind the problem of interest. CINEMA aims to providing a training platform that will allow the next generation of polymer scientists to take polymer science into the 21st century through incorporating the fundamental knowledge gained over many years of research into the training of machine learning systems. Such a knowledge-driven machine learning approach puts the scientific issues of CINEMA at the forefront of the use of machine learning in fundamental scientific problems, and also provides the perfect training platform for the next generation of scientists, for whom the use of AI will be an invaluable tool.