Automated Search for Materials for Ammonia Synthesis and Water Splitting

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

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.