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D3SC and EAGER: Using Deep Learning to Find Algorithms for Optimizing Chemical Reactions

D3SC 和 EAGER：利用深度学习寻找优化化学反应的算法

基本信息

批准号：
1734082
负责人：
Richard Zare
金额：
$ 20.97万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734082&HistoricalAwards=false
关键词：
D3SC EAGER Using Deep Learning

项目摘要

With support from the Chemical Structure, Dynamics and Mechanisms - B Program in the Division of Chemistry and in response to the Data-Driven Discovery Science in Chemistry (D3SC) Dear Colleague Letter, Professor Richard N. Zare at Stanford University is working on optimizing chemical reactions in microdroplets with deep reinforcement learning. Unoptimized reactions are expensive because they waste time and reagents. A common way for chemists to explore reaction optimization is to change one variable at a time while all other variables remain fixed. This method, however, might not find the best conditions, that is the global optimum. Another way is to search across all combinations of reaction conditions by using batch chemistry. This approach gives a better chance to find the global optimal condition, but it is time-consuming and expensive. Deep reinforcement learning is believed to be a superior approach in which the computer analyzes a large data set and recognizes the pattern of features that lead to best reaction outcomes. It is like training a dog: suppose we want the dog to pick up a ball. If the dog does what we want, we say "Good dog!"; if it does not, we say "Bad dog!". Similarly, Professor Zare uses a machine learning method to give the system a positive reward if the reaction reaches a better result than previous ones, or a negative reward if it does not. A repeated process will eventually result in a set of best reaction conditions for certain reactions. Professor Zare and his group apply this approach to microdroplet chemistry, where many reactions can be carried out in small droplets and be accelerated by factors of one thousand to one million compared with the same reaction happening in bulk solution. Combining the efficient deep reinforcement learning method with accelerated microdroplet reactions, Professor Zare and his group are seeking to find optimal reaction conditions in a fast way. This combined approach can represent a significant step for enabling artificial intelligence to be used to optimize chemical reactions, which should have benefits in chemical production, drug screening, and materials discovery. The students in the Zare group enjoy the unique opportunity to experience micro-droplet chemical synthesis, fast chemical characterization, and deep learning-based complex data analysis.A reaction can be thought of as a system having multiple inputs (parameters) and providing one or more outputs. Example inputs include: temperature; solvent composition; pH; catalyst; droplet size; and time. Example outputs include: product yield; selectivity; purity; and cost. The goal of reaction optimization described here is to select the best inputs to achieve a given output, which can be formulated as a reinforcement learning system. In order to find the optimal reaction conditions, Professor Zare is searching for critical reaction condition to try at the next step based on previous reaction conditions and product yields. A recurrent neural network is used to model the policy for reaction optimization. The reinforcement learning system is trained on mock reactions (random functions) and then real reactions for better performance. The approach, if successful, could help better understanding of fundamental features of reactivity and enable important industrial applications.

斯坦福大学的Richard N. Zare教授在化学系化学结构、动力学和机制B项目的支持下，响应数据驱动的化学发现科学（D3SC）致同事的信，正在利用深度强化学习优化微滴中的化学反应。未优化的反应是昂贵的，因为它们浪费时间和试剂。化学家探索反应优化的一种常用方法是每次改变一个变量，而所有其他变量保持不变。然而，这种方法可能找不到最佳条件，即全局最优。另一种方法是通过使用批化学来搜索所有反应条件的组合。这种方法提供了更好的机会来找到全局最优条件，但它既耗时又昂贵。深度强化学习被认为是一种优越的方法，在这种方法中，计算机分析大量数据集并识别导致最佳反应结果的特征模式。这就像训练一只狗：假设我们想让狗捡起一个球。如果狗狗做了我们想让它做的事，我们会说：“好狗！”如果没有，我们就说“坏狗！”同样，Zare教授使用一种机器学习方法，如果反应达到比之前更好的结果，就给系统一个积极的奖励，如果没有，就给系统一个消极的奖励。一个重复的过程将最终产生一组对某些反应的最佳反应条件。Zare教授和他的团队将这种方法应用于微液滴化学，其中许多反应可以在小液滴中进行，并且与在大量溶液中发生的相同反应相比，速度可以加快1000到100万倍。将高效的深度强化学习方法与加速微滴反应相结合，Zare教授和他的团队正在寻求快速找到最佳反应条件。这种结合的方法可以代表使人工智能用于优化化学反应的重要一步，这应该在化学生产，药物筛选和材料发现方面有好处。Zare小组的学生有独特的机会体验微液滴化学合成、快速化学表征和基于深度学习的复杂数据分析。反应可以被认为是一个具有多个输入（参数）并提供一个或多个输出的系统。示例输入包括：温度；溶剂组成;pH值;催化剂;液滴的大小;和时间。示例输出包括：产品产量；选择性;纯洁;和成本。这里描述的反应优化的目标是选择最佳的输入来实现给定的输出，这可以被表述为一个强化学习系统。为了找到最佳的反应条件，Zare教授正在根据之前的反应条件和产物产率寻找下一步尝试的临界反应条件。采用递归神经网络对反应优化策略进行建模。强化学习系统先训练模拟反应（随机函数），然后训练真实反应，以获得更好的性能。该方法如果成功，将有助于更好地理解反应性的基本特征，并使重要的工业应用成为可能。