CDS&E: Chemist-Machine Collaborations for Reaction Mechanism Discovery

在化学系化学理论、模型和计算方法项目的支持下，密歇根大学的 Paul Zimmerman 教授正在开发所谓的化学家循环机器学习方法，用于预测化学反应的结果。 只要深入了解化学反应的机制，就可以控制和调整化学反应以达到理想的结果。这些信息可以通过第一原理计算工具获得，这些工具以其探索和解释反应机制的能力而闻名。近年来，出现了传统计算模型的替代方案，特别是机器学习模型，它在识别数据集中隐藏的模式方面非常有用。由于第一原理方法是化学行为的神谕，因此该项目设想将机器学习方法与第一原理技术相结合，以快速、彻底地探索化学空间。齐默尔曼研究小组将开发和测试涉及这两种方法类型的新混合策略，并将其应用于新兴的、人们知之甚少的化学转化。总体项目策略不仅将导致化学反应机制的发现，还将提供一个环境来培训年轻科学家的机制开发艺术。特别是，该项目的研究生将学习数据科学方法、软件开发，并帮助指导参与研究活动的神经多样性实习生。这些学习活动对于下一代劳动力的发展至关重要，来自不同背景的个人利用互补的技能组合在一起工作，以推动科学成果。Zimmerman 小组将先进的机器学习方法与基于图形的反应发现工具相结合，后者提供训练前者所需的数据。这两种策略可以通过主动迁移学习和化学家在环学习来协同作用，以克服反应发现任务中固有的数据稀缺性。主动迁移学习利用小数据快速训练机器学习模型，并且最好通过化学家的专业知识来了解，他们可以将物理相关的因果推论与单纯的统计相关性区分开来。因此，这些方法相互支持，形成一种策略，减少复杂反应网络中假设基本步骤的第一原理评估的计算负担。有了这些基本策略，Zimmerman 小组将致力于开发一个反应浏览器界面，将新方法整合在一起，并使化学家能够轻松使用它们。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Paul Zimmerman of the University of Michigan is developing so-called chemist-in-the-loop machine learning approaches for predicting the outcomes of chemical reactions. Chemical reactions can be controlled and tuned to desirable outcomes whenever deep knowledge of their mechanisms is available. This information can be gained through first principles computational tools, which are well known for their ability to explore and explain reaction mechanisms. In recent years, alternatives to conventional computational models have appeared, in particular machine learning models which are highly useful at recognizing patterns hiding within datasets. Since first principles methods are an oracle for chemical behavior, this project envisions the combination of machine learning methods with first principles techniques to quickly and thoroughly explore chemical space. The Zimmerman research group will develop and test new hybrid strategies involving the two method types, and apply them to emerging, poorly understood chemical transformations. The overall project strategy will not only lead to discovery of chemical reaction mechanisms, but will provide an environment to train young scientists in the art of mechanism development. In particular, graduate students on the project will learn data science methods, software development, and help mentor neurodiverse interns that will participate in the research activities. These learning activities are crucial to the development of a next-generation workforce, where individuals from diverse backgrounds work together using complementary skillsets to push forward scientific outcomes.The Zimmerman group will combine advanced machine learning methods with graph-based reaction discovery tools, where the latter provides the data needed to train the former. These two strategies can be synergized through active-transfer learning and chemist-in-the-loop learning to overcome the data scarcity that is inherent in reaction discovery tasks. Active-transfer learning leverages small data to quickly train machine learning models, and is best informed by expert knowledge coming from chemists who can separate physically relevant, causal inferences from mere statistical correlations. These approaches therefore support each other, forming a strategy for reducing the computational burden of first principles evaluation of hypothetical elementary steps within complicated reaction networks. With these fundamental strategies in hand, the Zimmerman group will work to develop a reaction explorer interface to stitch together the new approaches and make them easily accessible to chemists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.