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

DMREF：协作研究：合成基因组：新材料合成的数据挖掘

• 批准号: 1922090
• 负责人: Andrew McCallum
• 金额: $ 40万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922090&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Synthesis; Genome

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.

加速材料设计的成功部分通过了材料基因组计划，使材料发育中的瓶颈转向了新化合物的合成。现有数据库不包含有关制作通过计算方法设计的有希望属性的化合物所需的合成配方的信息。结果，在设计过程中获得的大部分动力和效率都通过反复试验的合成技术进​​行了门控。从有希望的材料概念到验证，优化和扩大规模的这种延迟是新颖材料商业化的重大负担。这种设计材料彻底改变和设计我们的未来（DMREF）研究将建立合成的预测工具，以便可以在几天而不是数月或几年的时间内综合具有有趣属性的化合物的开发时间。研究活动包括从已发表的文献中自动提取信息，以及有关使用自然语言处理技术如何制作固体无机材料的专利。在此文本提取之后，该项目将生成材料合成食谱的“食谱”。可以通过机器学习方法开采该食谱，以获取有关如何通过在先前制造的材料中寻找图案和相似性来制作新材料的建议。项目结果将是材料综合方法的数据集，可供社区提供。另一个关键的项目结果是使用机器学习来预测材料的新颖或优化食谱。这些预测将伴随着对称为沸石的一类材料的实验确认。这项研究的外展部分的主要目标是使非专家使用数据库。 这将通过在线教程和个人讲习班来完成。 在线教程将教授利用在线工具和功能所需的基本知识，而研讨会将向想要利用数据库本身的学生和研究人员介绍。从机器学习的角度，将对文献中自动提取信息的方法进行半监督。将使用无监督的方法，包括捕获科学语料库中单词上下文的单词嵌入。然后，下游监督方法将通过单词的类型及其与其他单词的关系来对单词进行分类。这构成了食谱数据库的基础。然后，将使用材料信息学界的机器学习工具来开采提取的信息。因为从NLP的角度来看，配方分类（随后描述）利用专业知识，而目标材料分类从材料的角度利用了专业知识，因此这种跨学科方法具有很大的杠杆作用，这是一种跨学科的方法，以前没有追求的伙伴关系来实现更多的材料设计。这种方法基于既定的合成知识，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AB-Initio热化学数据可用性相结合。这些不同领域的整合将为合成方法的更合理设计提供直接的途径，从而大大加快了新材料概念的部署和测试。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得支持的。

项目成果

Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

• DOI: 10.18653/v1/2022.naacl-main.331
• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Sheshera Mysore;Arman Cohan;Tom Hope

Unsupervised Partial Sentence Matching for Cited Text Identification

• 发表时间: 2022
• 作者: Kathryn Ricci;Haw-Shiuan Chang;Purujit Goyal;A. McCallum

Augmenting Scientific Creativity with Retrieval across Knowledge Domains

通过跨知识领域的检索增强科学创造力

• 发表时间: 2022
• 期刊: NLP+HCI Workshop at North American Chapter of the Association for Computational Linguistics 2022
• 作者: Kang, Hyeonsu B.;Mysore, Sheshera;Huang, Kevin;Chang, Haw-Shiuan;Prein, Thorben;McCallum, Andrew;Kittur, Aniket;Olivetti, Elsa
• 通讯作者: Olivetti, Elsa

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

• DOI: 10.1021/acs.jcim.9b00995
• 发表时间: 2020-03-23
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Kim, Edward;Jensen, Zach;Olivetti, Elsa
• 通讯作者: Olivetti, Elsa

Editable User Profiles for Controllable Text Recommendations

用于可控文本推荐的可编辑用户配置文件

• DOI: 10.1145/3539618
• 发表时间: 2023
• 期刊: ACM
• 作者: Mysore, Sheshera;Jasim, Mahmood;McCallum, Andrew;Zamani, Hamed
• 通讯作者: Zamani, Hamed