DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials

加速材料设计的成功，部分是通过材料基因组计划实现的，已经将材料开发的瓶颈转向了新化合物的合成。现有的数据库不包含有关合成配方的信息，这些合成配方是通过计算方法设计的，用来制造具有前景的化合物所必需的。因此，在设计过程中获得的大部分动力和效率都受到了试错综合技术的限制。这种从有前途的材料概念到验证、优化和规模化的延迟是新材料商业化的重大负担。这项旨在革新和设计我们未来的设计材料（DMREF）研究将为合成建立预测工具，以便具有有趣性质的化合物的开发时间可以在几天内合成，而不是几个月或几年。研究活动包括利用自然语言处理技术从已发表的文献和专利中自动提取有关固体无机材料如何在过去制造的信息。在此文本提取之后，该项目将生成一个材料合成食谱的“食谱”。这本食谱可以通过机器学习方法来挖掘如何通过寻找以前制作的材料之间的模式和相似性来制作新材料的建议。该项目的成果将是向社区提供一套材料综合方法的数据集。另一个关键的项目成果是使用机器学习来预测新的或优化的材料配方。这些预测将伴随着一种被称为沸石的催化材料的实验证实。这项研究的外联部分的主要目标是使非专家能够使用该数据库。这将通过在线教程和面对面的研讨会来完成。在线教程将教授使用在线工具和功能所需的基本知识，而研讨会将针对想要使用数据库本身的学生和研究人员。从机器学习的角度来看，文献中信息的自动提取方法将是半监督的。将使用非监督方法，包括捕获科学语料库中单词上下文的词嵌入。然后使用下游监督方法根据单词的类型和与其他单词的关系对单词进行分类。这构成了配方数据库的基础。然后，提取的信息将使用材料信息学社区的机器学习工具进行挖掘。由于配方分类（随后描述）利用了NLP角度的专业知识，而目标材料分类利用了材料角度的专业知识，因此从这种跨学科方法中可以获得重要的杠杆作用，这种伙伴关系以前没有追求进一步的材料设计。该方法建立在已建立的合成知识基础上，并将其与现代数据提取、材料信息学、文本挖掘和机器学习技术以及高通量从头算热化学数据可用性相结合。这些不同领域的整合将为更合理的合成方法设计提供直接途径，从而显著加快新材料概念的部署和测试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Successes in accelerated materials design, made possible in part through the Materials Genome Initiative, have shifted the bottleneck in materials development towards the synthesis of novel compounds. Existing databases do not contain information about the synthesis recipes necessary to make compounds that are found to have promising properties, designed through computational methods. As a result, much of the momentum and efficiency gained in the design process becomes gated by trial-and-error synthesis techniques. This delay in going from promising materials concept to validation, optimization, and scale-up is a significant burden to the commercialization of novel materials. This Designing Materials to Revolutionize and Engineer our Future (DMREF) research will build predictive tools for synthesis so that the development time for chemical compounds with interesting properties can be synthesized in a matter of days, rather than months or years. The research activities include automatically extracting information from the published literature and patents on how solid inorganic materials have been made in the past by using natural language processing techniques. After this text extraction the project will generate a "cookbook" of materials synthesis recipes. This cookbook can be mined through machine learning approaches for suggestions on how to make new materials by looking for patterns and similarities among previously made materials. The project outcome will be a data set of materials synthesis methods, to be made available to the community. Another key project outcome is to use machine learning to predict novel or optimized recipes for materials. These predictions will be accompanied by experimental confirmation for a class of materials used in catalysis called zeolites. The major objective of the outreach component of this research is to enable the use of the database by non-experts. This will be accomplished through both online tutorials and in person workshops. The online tutorials will teach the basic knowledge required to utilize the online tools and functionalities while the workshops will be addressed to students and researchers who want to make use of the database itself. The approach to automatic extraction of information in the literature will be semi-supervised from a machine learning perspective. Unsupervised methods, including word embeddings that capture the context of words within scientific corpus, will be used. Then downstream supervised methods will be used to classify words by their type and their relationship to other words. This forms the basis of the recipe database. The extracted information will then be mined using machine learning tools from the materials informatics community. Because the recipe classification (described subsequently) leverages expertise from the NLP perspective and the target material classification leverages expertise from the materials perspective, there is significant leverage to be had from this interdisciplinary approach, a partnership not previously pursued to further materials design. This approach builds on established synthesis knowledge, and combines it with modern data extraction, materials informatics, text mining and machine learning techniques, and high-throughput ab-initio thermochemical data availability. The integration of these different fields will provide a direct route towards more rational design of synthesis methods and thereby significantly accelerate the deployment and testing of new materials concepts.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.