Charles Musgrave of the University of Colorado Boulder and collaborator Aaron Holder are supported by the Division of Chemistry, and the Division of Chemical, Bioengineering, Environmental, and Transport Systems, to develop and apply machine learning approaches for the discovery of new materials. While the periodic table provides a many possible combinations of elements from which to form materials, only a fraction of these compounds will be stable or have desirable properties for particular applications. Furthermore, of the large number of possible compounds, only about one thousand have known properties at elevated temperatures. For the past fifty years, computational chemists have used equations of quantum mechanics to discover new materials. However, screening large numbers of candidate materials for a specific technological application remains too computationally demanding to be practical. Recently, statistical learning approaches have been developed which can extract systematic information from large quantities of data to train highly reliable "artificial intelligence" models for predicting properties of a new system. In this project, Professors Musgrave and Holder are using machine learning approaches applied to predict the stabilities, structures, and chemical reactivity 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 available on public repositories as machine learning computer codes, and through publicly-accessible databases. The project is training high school, undergraduate and graduate students in the development and application of state-of-the-art machine learning methods for chemistry and chemical engineering applications. The researchers participation in the Broadening Opportunity through the Leadership and Diversity (BOLD) Center at University of Colorado. The incorporation of new concepts in machine learning and chemistry are integrated into courses and through the departmental LearnChemE YouTube platform.
This project combines expertise in electronic structure, thermodynamics, computational science, and machine learning to study one of the most fundamental properties of molecules--the Gibbs free energy, G(T). The data-driven approach takes advantage of results showing that the vibrational entropy and Helmholtz free energy computed in the constant-volume quasiharmonic approximation - quantities that critically contribute to G(T) but are computationally challenging to calculate quantum-mechanically - have systematic temperature dependence and can be accurately and efficiently predicted using machine learning, coupled with knowledge of the chemical composition of the material. The researchers are extending this observation to apply machine learning methods to model G(T) directly, using experimental data for several hundred molecules for training and descriptor extraction. The resulting descriptors are being used to predict thermochemical data for ~20,000 unique compositions tabulated in the Inorganic Crystal Structure Database, and in turn, to compute temperature-dependent convex hull phase diagrams and solid-state reaction equilibria. The models and G(T) data are available on large databases. The new methodology is enabling the discovery of general trends and new chemical knowledge of the effects of temperature and composition on reactivity, synthesizability, stability and metastability. In addition to providing deep insights into the thermochemistry of molecules and reactions, this research is enabling the identification of anomalies that may indicate systems where emerging properties are altering the behavior of the molecule. For example, where temperature-dependent emergent or quantum phenomena create unique materials properties. Despite the technological and economic importance of advanced materials in a broad range of technologies, much is still unknown about the detailed behavior that give rise to their stability and reactivity. Potential applications of the new techniques and thermochemical databases produced include thermochemical water splitting using redox materials, ammonia synthesis by chemical looping, oxidation chemistries, carbothermal reduction of oxides, and reduction of molecules by molecular hydrogen or other reductants.
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.
