CDS&E: D3SC: The Dark Reaction Project: A machine-learning approach to exploring structural diversity in solid state synthesis

CDS

• 批准号: 1928882
• 负责人: Joshua Schrier
• 金额: $ 55.27万
• 依托单位: Fordham University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928882&HistoricalAwards=false
• 关键词: CDS& D3SC; Dark; Reaction; Project

NONTECHNICAL SUMMARYThis award receives funds from the Division of Materials Research, the Chemistry Division and the Office of Advanced Cyberinfrastructure. This award supports research and education that uses data-centric methods to enable the prediction of metal oxide compounds with desired properties. Organically-templated metal oxides have a tremendous degree of structural diversity and compositional flexibility. This allows chemists to tune the structures, properties, and symmetries of these compounds to optimize their performance in specific applications that include catalysis, molecular sieving, gas adsorption, and nonlinear optics. However, new compounds are typically created by a trial-and-error procedure, and creating novel compounds with specific structures is a grand challenge in solid state chemistry. This project will develop artificial intelligence techniques for computers called machine learning techniques that can be used to predict the conditions for chemical reactions that will increase structural diversity and lead to specific structural features. This project will also develop machine learning techniques that generate human-readable explanations about the formation mechanism, which will be tested in the laboratory. The primary impact of this project will be to decrease the amount of time and to lower the cost of discovering new materials with specific structural features, which in turn help bring new materials for applications to market more quickly. This project is an example of a collaboration among synthetic chemists, computational chemists, and computer scientists and as a model it may be directly transferred to a wide range of disciplines and avenues of investigation. Undergraduate student research opportunities and curricular developments will be involved throughout the project, thus contributing to the scientific workforce. TECHNICAL SUMMARY This award receives funds from the Division of Materials Research, the Chemistry Division and the Office of Advanced Cyberinfrastructure. This award supports research and education that uses data-centric methods to enable the prediction of metal oxide compounds with desired properties. Hydrothermal synthesis is widely used to create new metal oxide materials with a wide range of functional properties and applications. This project will advance the field by developing software infrastructure for associating the results of X-ray diffraction experiments with individual reactions, extracting structural outcome descriptors from this data, and then determining the extent to which these structural outcomes can be predicted from reaction description data. This will be achieved by developing structural outcome descriptors for geometric properties, non-covalent interaction properties, and electron-density properties, then building machine learning models that correlate these outcomes to reaction conditions, and finally testing the quality of these predictions experimentally. Active learning and auditable and interpretable models will be incorporated into the workflows to help synthetic chemists select better (more insightful/novel) reactions in an interactive fashion.

非技术摘要这一奖项获得了材料研究部，化学部和高级网络基础设施办公室的资金。该奖项支持使用以数据为中心的方法的研究和教育来实现具有所需特性的金属氧化物化合物的预测。有机的金属氧化物具有巨大的结构多样性和组成灵活性。这使化学家可以调整这些化合物的结构，性质和对称性，以优化其在特定应用中的性能，包括催化，分子筛分，气体吸附和非线性光学元件。 但是，新化合物通常是由试验和错误程序创建的，并且具有特定结构的新型化合物是固态化学的巨大挑战。 该项目将针对称为机器学习技术的计算机开发人工智能技术，这些技术可用于预测化学反应的条件，以增加结构多样性并导致特定的结构特征。 该项目还将开发机器学习技术，从而产生有关形成机制的人类可读解释，该解释将在实验室中进行测试。该项目的主要影响是减少时间量，并降低具有特定结构特征的新材料的成本，这反过来又有助于将新材料更快地推向市场上。 该项目是合成化学家，计算化学家和计算机科学家之间合作的一个例子，作为模型，它可以直接转移到广泛的学科和研究途径上。在整个项目中将涉及本科生的研究机会和课程发展，从而为科学劳动力做出了贡献。技术摘要该奖项获得了材料研究部，化学部和高级网络基础设施办公室的资金。该奖项支持使用以数据为中心的方法的研究和教育来实现具有所需特性的金属氧化物化合物的预测。水热合成被广泛用于创建具有广泛功能特性和应用的新金属氧化物材料。 该项目将通过开发软件基础架构来推进该领域，以将X射线衍射实验的结果与单个反应相关联，从该数据中提取结构性结果描述，然后确定可以从反应描述数据中预测这些结构结果的程度。 这将通过为几何特性，非共价相互作用和电子密度特性开发结构性结果描述来实现，然后构建将这些结果与反应条件相关的机器学习模型，并最终通过实验测试这些预测的质量。 积极的学习和可审核和可解释的模型将被纳入工作流程中，以帮助合成化学家以互动方式选择更好的（更有见地/新颖的）反应。

项目成果

Enhanced Electrocatalytic Oxidation of Small Organic Molecules on Platinum-Gold Nanowires: Influence of the Surface Structure and Pt-Pt/Pt-Au Pair Site Density

• DOI: 10.1021/acsami.1c17244
• 发表时间: 2021-12-10
• 期刊: ACS APPLIED MATERIALS & INTERFACES
• 影响因子: 9.5
• 作者: Smina, Nicole;Rosen, Adam;Koenigsmann, Christopher
• 通讯作者: Koenigsmann, Christopher

The case for data science in experimental chemistry: examples and recommendations

• DOI: 10.1038/s41570-022-00382-w
• 发表时间: 2022-04-21
• 期刊: NATURE REVIEWS CHEMISTRY
• 影响因子: 36.3
• 作者: Yano, Junko;Gaffney, Kelly J.;Toma, Francesca M.
• 通讯作者: Toma, Francesca M.

Assessing the Local Interpretability of Machine Learning Models.

评估机器学习模型的本地可解释性。

• 发表时间: 2019
• 期刊: NeurIPS Workshop on Human-Centric Machine Learning (HCML
• 作者: Slack, D.;Friedler, S.A.;Roy, C.D.;Scheidegger, C.
• 通讯作者: Scheidegger, C.

Determining the Activity Series with the Fewest Experiments Using Sorting Algorithms

使用排序算法以最少的实验确定活动系列

• DOI: 10.1021/acs.jchemed.1c00043
• 发表时间: 2021
• 期刊: Journal of Chemical Education
• 影响因子: 3
• 作者: Schrier, Joshua;Tynes, Michael F.;Cain, Lillian
• 通讯作者: Cain, Lillian

Autonomous experimentation systems for materials development: A community perspective

• DOI: 10.1016/j.matt.2021.06.036
• 发表时间: 2021-07
• 作者: E. Stach;Brian L. DeCost;A. Kusne;J. Hattrick-Simpers;Keith A. Brown;Kristofer G. Reyes;Joshua Schrier;S. Billinge;T. Buonassisi;Ian T Foster;Carla P. Gomes;J. Gregoire;Apurva Mehta;Joseph H. Montoya;E. Olivetti;Chiwoo Park;E. Rotenberg;S. Saikin;S. Smullin;V. Stanev;B. Maruyama