The Dark Reaction Project: A Machine Learning Approach to Materials Discovery

有机模板无机固体的水热合成反应是数据驱动材料化学的理想测试案例，因为只需几种反应物（一种或两种无机组分，一种或两种有机组分和溶剂）和几种反应条件（pH值，温度，反应时间）就可以产生具有众多应用的多种产品。 尽管如此简单，但结晶产物的形成敏感地取决于所用试剂的量和反应条件，这使得这成为预测反应成功或失败的苛刻测试案例。 此外，与其他系统如金属有机框架（MOF）不同，存在的许多不同类型的分子间相互作用导致高度多样化的晶体结构，这无法事先预测。 我们的目标不是预测最终的晶体结构，而是解决一个更简单的问题，即反应是否会产生任何结晶产物。 我们的项目将通过三种策略来解决这个问题：我们建议为“暗反应”构建一个可搜索的在线知识库，这些反应已经在实验室笔记本中进行并记录，但从未在文献中报道。这首先是将我们自己的反应放到网上，然后是选定的实验合作者的反应，最后是创建一个可通过网络访问的公共存储库，用于存储、检索和利用反应信息。 利用这些数据，我们建议使用机器学习来获得预测，以提高执行新反应的成功率。 根据实验反应数据，我们使用化学信息学计算来预测单个试剂的200个计算性质（例如，货车德瓦尔斯表面积、极性表面积作为pH、氢键供体和受体的数量等的函数） 并计算50个化学计量描述符（例如，有机和无机组分的比例，通过氢键供体/受体作为pH的函数加权等）。基于506个反应的初步数据集，我们已经能够训练决策树模型，以达到87%的成功率来预测晶体产物是否形成。在这个项目中，我们将使改进后的模型公开（通过网络），解决物理化学模型中的弱点，并将其与商业上可用的起始材料数据库相结合。 最后，我们将进行实验验证，以证明新化合物的原理性合成，解决数据集中的结构漏洞，并与更广泛的研究团体合作，指导其他实验室的实验，以合成新材料并解决我们模型的化学空间的限制。 除了对材料合成这一特定领域的影响外，我们开发的软件架构将作为其他人开始类似项目的起点，我们承诺通过在允许其在学术环境中免费使用的许可下开源我们的代码，将我们的工作免费分发给其他人。 非技术总结：有机模板金属氧化物框架化合物具有突出的结构和化学多样性，这使它们适用于工业催化，气体分离和光学工程。 然而，尽管经过几十年的实验努力，制造这些材料的新例子仍然是一个耗时的试错过程。 大多数已经进行的化学反应被认为是“不成功的”，因为它们不会产生结晶产物，并且从未在文献中报道过。 没有收集这些实验的论坛，也没有从中获得价值的手段。 然而，这些“暗反应”是有价值的，因为它们定义了成功生产产物所需的反应条件的界限。通过提供一个可搜索的反应数据在线存储库，我们将能够更好地管理和共享这些暗反应。 此外，我们将使用这些数据作为训练机器学习（也称为统计学习或数据挖掘）算法的资源，这些算法可以提前预测反应的成功。 基于机器学习预测，我们将进行实验验证，以测试模型的预测。我们的项目将提供一种机制，用于收集暗反应，然后使用它们来指导未来的反应更加成功，从而减少研究人员的时间和合成新材料所需的试剂成本。 这将加速和降低发现新材料的成本（研究人员的时间和材料）。 这直接响应了白宫科技政策办公室2011年材料基因组计划的号召，特别是找到利用计算将功能材料更快推向市场的方法。 其次，该项目将作为化学家和计算机科学家之间合作的典范，可以直接转移到广泛的其他学科和研究途径。 第三，我们将为本科生提供一个有凝聚力的，全面的，跨学科的和持续的研究经验，从而有助于科学劳动力。 第四，我们的外联活动将促进对数据驱动技术的兴趣，建立一个合作实验室网络，并向希望启动相关项目的其他人提供软件基础设施。

Technical Summary:Hydrothermal synthesis reactions of organically-templated inorganic solids are an ideal test case for data-driven materials chemistry, as just a few reactants (one or two inorganic components, one or two organic components and solvent) and a few reaction conditions (pH, temperature, reaction time) yield a diversity of products with numerous applications. Despite this simplicity, the formation of crystalline products depends sensitively on the quantities of reagents used and the reaction conditions, which makes this a demanding test case for predicting success or failure of the reaction. Moreover, unlike other systems such as metal organic frameworks (MOFs), the many different types of intermolecular interactions that are present result in highly diverse crystal structures which cannot be predicted a priori. Rather than predicting a final crystal structure, we aim to address the simpler problem of whether a reaction will yield any crystalline product or not. Our project will address this with three strategies: We propose constructing a searchable online repository for "dark reactions", the chemical reactions that have been performed and recorded in laboratory notebooks, but never reported in the literature. This begins with putting our own reactions online, then the reactions of selected experimental collaborators, and finally creating a web-accessible public repository for depositing, retrieving, and utilizing reaction information. Using this data, we propose using machine learning to derive predictions to increase the success rate of performing novel reactions. From the experimental reaction data, we use cheminformatics calculations to predict 200 computed properties of the individual reagents (e.g., van der Waals surface areas, polar surface areas as a function of pH, number of hydrogen bond donors and acceptors, etc.) and compute 50 stoichiometric descriptors (e.g., ratios of organic and inorganic components, weighted by hydrogen bond donor/acceptors as a function of pH, etc.). Based on a preliminary dataset of 506 reactions, we have been able to train a decision tree model to achieve an 87% success rate in predicting whether a crystalline product is formed or not. During this project, we will make the improved model publicly available (via the web), address weaknesses in the physicochemical model, and integrate this with databases of commercially available starting materials. Finally, we will perform experimental validation to demonstrate a proof-of-principle synthesis of new compounds, address structural holes in the dataset, and engage with the broader research community to guide experiments in other laboratories for the synthesis of new materials and addressing limitations in the chemical space of our model. Besides the impact on this specific area of materials synthesis, the software architecture that we develop will serve as a starting point for others to begin similar projects and we commit to freely distributing our work to others by open-sourcing our code under a license that will allow its free use in academic settings. Non-technical Summary: Organically-templated metal oxide framework compounds have outstanding structural and chemical diversity, which lends them to applications for industrial catalysis, gas separation, and optical engineering. Yet, despite several decades of experimental effort, making new examples of these materials is a time-consuming trial-and-error process. Most of the chemical reactions that have been performed are deemed "unsuccessful" because they do not result in a crystalline product, and are never reported in the literature. There is no forum for collecting these experiments, nor a means for deriving value from them. Nevertheless, these "dark reactions" are valuable because they define the bounds on the reaction conditions needed to successfully produce a product. By providing a searchable online repository for reaction data, we will enable better management and sharing of these dark reactions. Moreover, we will use this data as a resource to train machine learning (aka statistical learning or data-mining) algorithms that predict the success of reactions ahead of time. Based on the machine learning predictions, we will perform experimental validation to test the predictions of the model.Our project will provide a mechanism for collecting the dark reactions and then using them to guide future reactions to be more successful, reducing the researcher time and cost of reagents needed to synthesize new materials. This will accelerate and lower the cost (in researcher time and materials) of discovering new materials. This directly addresses the call of the White House Office of Science and Technology Policy's 2011 Materials Genome Initiative, specifically finding ways to use computation to bring functional materials to market more quickly. Second, this project will serve as a model for collaboration between chemists and computer scientists that can be directly transferred to a wide range of other disciplines and avenues of investigation. Third, we will provide a cohesive, comprehensive, interdisciplinary and sustained research experience for undergraduate students, thus contributing to the scientific workforce. Fourth, our outreach activities will foster interest in data-driven techniques, create a network of collaborating laboratories and provide the software infrastructure to others wishing to initiate related projects.