Automated Search for Materials for Ammonia Synthesis and Water Splitting

自动搜索氨合成和水分解材料

• 批准号: 1806079
• 负责人: Charles Musgrave
• 金额: $ 13.63万
• 依托单位: University of Colorado at Boulder
• 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: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1806079&HistoricalAwards=false
• 关键词: Automated; Search; Materials; Ammonia; Synthesis

The use of renewable electricity and solar energy to convert water, carbon dioxide, and nitrogen into energy-dense fuels and high-valued chemicals can improve the storage and utilization of intermittent solar and wind energy. This technology for "solar fuels" has benefits in utilization of renewable energy sources into value added chemicals used to make industrial products. This project supports fundamental research of the discovery of advanced catalysts for a wide range of redox reactions. When conducting new materials discovery, for a given family of promising material compositions, only a fraction of materials will have desirable properties for the targeted reaction applications. For many of these solid-state materials, the chemical equilibria and driving forces for chemical reactions are unknown. Statistical learning approaches have been developed which can extract information from large quantities of data to train highly reliable "artificial intelligence" models for predicting properties of a new material system. In this project, the principal investigators are using machine learning approaches applied to experimental data for hundreds of materials to predict the stabilities, structures, and chemical reactivity of hundreds of materials. The predicted properties can then be used to identify candidate materials for catalyzing technologically-important reactions, such as splitting water into oxygen and hydrogen, converting carbon dioxide into useful products, or the 'green' synthesis of ammonia from nitrogen and water. The models are being made available on public repositories such as machine learning computer codes, and through publicly-accessible materials databases. The project is training high school, undergraduate and graduate students in the application of state-of-the-art machine learning methods for chemistry, chemical engineering, and materials science applications. The research is integrated with education and outreach through the PI's participation in the Broadening Opportunity through the Leadership and Diversity (BOLD) Center at University of Colorado, and the incorporation of new concepts in machine learning and chemistry within the PI's courses. This project will apply machine learning approaches for the discovery of new oxide and oxynitride materials at scale for catalyzing splitting water into oxygen and hydrogen or the 'green' synthesis of ammonia from nitrogen and water. The chemical driving forces for the reactions involved in splitting water and ammonia synthesis depend critically on the energy to create an oxygen vacancy in the oxide or oxynitride material. In this project, The PI is using machine learning approaches trained on a set of oxygen vacancy formation energies that were calculated quantum mechanically. This project is complementary and leverages grant CHE 1800592 that focuses on the development of the machine learning methods and datasets. The predicted properties can then be used to identify candidate oxides and oxynitrides for catalyzing splitting water or ammonia synthesis. This project combines expertise in electronic structure, thermodynamics, computational materials science, and machine learning to study a central property of oxides - their oxygen vacancy formation energies, EV. The data-driven approach takes advantage of results showing that EV depends systematically on various materials properties, such as the electronic band gap and the enthalpy of formation of the material. The researchers will apply machine learning methods to model EV directly, using quantum mechanically calculated EV data for several hundred materials for training and descriptor extraction. The resulting descriptors are being used to predict EV for unique oxide and nitride compositions, and in turn, will enable the computation of millions of reaction equilibria for the oxidation and reduction reactions for water splitting and ammonia synthesis mediated by these materials. Despite the enormous technological and economic importance of advanced oxides and oxynitrides in a broad range of technologies, much is still unknown about the detailed behavior that give rise to their chemical properties. Potential applications of the new techniques and thermochemical databases produced by this project include thermochemical water splitting using redox materials, ammonia synthesis by chemical looping, oxidation of materials, the carbothermal reduction of oxides, oxygen separation membranes, and solid oxide fuel cell electrolytes.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.

将可再生电力和太阳能的使用将水，二氧化碳和氮转化为能量密集的燃料和高价值化学物质可以改善间歇性太阳能和风能的存储和利用。这项用于“太阳能”的技术在利用可再生能源中的利益中有益于用于生产工业产品的增值化学品。该项目支持针对各种氧化还原反应的高级催化剂发现的基本研究。在进行新材料发现时，对于给定有前途的材料组成的家族，只有一小部分材料对目标反应应用具有理想的特性。对于许多这些固态材料，化学平衡和化学反应的驱动力尚不清楚。已经开发了统计学习方法，可以从大量数据中提取信息，以培训高度可靠的“人工智能”模型，以预测新材料系统的性质。在该项目中，主要研究人员正在使用用于数百种材料实验数据的机器学习方法来预测数百种材料的稳定性，结构和化学反应性。然后，预测性能可用于鉴定催化技术重要反应的候选材料，例如将水分解为氧气和氢，将二氧化碳转化为有用的产物，或从氮和水中添加氨的“绿色”合成。这些模型可在公共存储库（例如机器学习计算机代码）以及通过公开访问的材料数据库中提供。该项目是在应用化学，化学工程和材料科学应用的最先进的机器学习方法中培训高中，本科和研究生。这项研究通过PI通过科罗拉多大学的领导力和多样性（BOLD）中心参与扩大机会，与教育和宣传融为一体，并在PI课程中纳入了机器学习和化学中的新概念。该项目将采用机器学习方法，以大规模发现新的氧化物和氧气材料，以将水分解为氧气和氢或氮和水的氨合成。与分裂水和氨合成涉及的反应的化学驱动力非常取决于能量，以在氧化物或氧气材料中产生氧气空位。在该项目中，PI使用了在机械计算量子的一组氧气空位形成能量上训练的机器学习方法。该项目是互补的，利用了Grant Che 1800592，重点是机器学习方法和数据集的开发。然后，预测的性质可用于鉴定候选氧化物和氧气，用于催化分裂水或氨合成。该项目结合了电子结构，热力学，计算材料科学和机器学习方面的专业知识，以研究氧化物的中心特性 - 其氧气空位形成能，EV。数据驱动的方法利用了结果，表明EV系统地取决于各种材料特性，例如电子带隙和材料形成的焓。研究人员将使用机械计算的EV数据将机器学习方法直接用于直接建模EV，以用于培训和描述符提取的数百材料。所得的描述符用于预测独特的氧化物和硝酸盐组成的EV，进而将使这些材料介导的水分分裂和氨合成的氧化和还原反应来计算数百万个反应平衡的计算。尽管在广泛的技术中，晚期氧化物和氧气的技术和经济重要性巨大，但对于引起其化学性质的详细行为仍然尚不清楚。该项目生成的新技术和热化学数据库的潜在应用包括使用氧化还原材料分割的热化学水，通过化学循环，氨合成氨合成，材料的氧化，氧化氧化物的减少，氧化物的减少，氧气分离膜和使用固体氧化物燃料电池的质量促进，并反映了nsf的旨意。优点和更广泛的影响审查标准。

项目成果

A Synergistic Approach to Unraveling the Thermodynamic Stability of Binary and Ternary Chevrel Phase Sulfides

• DOI: 10.1021/acs.chemmater.0c02648
• 发表时间: 2020-07
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: K. Lilova;J. Perryman;Nicholas R. Singstock;M. Abramchuk;T. Subramani;Andy Lam;Ray M. S. Yoo;Jessica C. Ortiz-Rodríguez;C. Musgrave;A. Navrotsky;J. Velázquez

High-Throughput Equilibrium Analysis of Active Materials for Solar Thermochemical Ammonia Synthesis

• DOI: 10.1021/acsami.9b01242
• 发表时间: 2019-07-17
• 期刊: ACS APPLIED MATERIALS & INTERFACES
• 影响因子: 9.5
• 作者: Bartel, Christopher J.;Rumptz, John R.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.

The role of decomposition reactions in assessing first-principles predictions of solid stability

• DOI: 10.1038/s41524-018-0143-2
• 发表时间: 2019-01-04
• 期刊: NPJ COMPUTATIONAL MATERIALS
• 影响因子: 9.7
• 作者: Bartel, Christopher J.;Weimer, Alan W.;Holder, Aaron M.
• 通讯作者: Holder, Aaron M.

High‐Throughput Analysis of Materials for Chemical Looping Processes

• DOI: 10.1002/aenm.202000685
• 发表时间: 2020-02
• 期刊: Advanced Energy Materials
• 影响因子: 27.8
• 作者: Nicholas R. Singstock;Christopher J. Bartel;A. Holder;C. Musgrave

Inorganic Halide Double Perovskites with Optoelectronic Properties Modulated by Sublattice Mixing

• DOI: 10.1021/jacs.9b12440
• 发表时间: 2020-03-18
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Bartel, Christopher J.;Clary, Jacob M.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.