Lauren Takahashi

Descriptors for predicting the lattice constant of body centered cubic crystal

The prediction of the lattice constant of binary body centered cubic crystals is performed in terms of first principle calculations and machine learning. In particular, 1541 binary body centered cubic crystals are calculated using density functional theory. Results from first principle calculations, corresponding information from periodic table, and mathematically tailored data are stored as a dataset. Data mining reveals seven descriptors which are key to determining the lattice constant where the contribution of descriptors is also discussed and visualized. Support vector regression (SVR) technique is implemented to train the data where the predicted lattice constants have the mean score of 83.6% accuracy via cross-validation and maximum error of 4% when compared to experimentally determined lattice constants. In addition, trained SVR is successful in predicting material combinations from a desired lattice constant. Thus, a set of descriptors for determining the lattice constant is identified and can be used as a base descriptor for lattice constants of further complex crystals. This would allow for the acceleration of the search for lattice constants of desired atomic compositions as well as the prediction of new materials based on a specified lattice constant.

Reactivity of Two-Dimensional Au9, Pt9, and Au18Pt18 against Common Molecules

Adsorption of common molecules over two-dimensional Au9, Pt9, and Au18Pt18 is investigated with implementation of first-principles calculations. In general, it is found that Pt9 and Au18Pt18 exhibit low adsorption energies where Au18Pt18 preserves the structural integrity of the molecule and surface. In particular, adsorption of molecules onto Au18Pt18 frequently results in low adsorption energies and high reactivity with minor surface reconstruction of Au18Pt18 and average bond lengths of molecules. The decrease in adsorption energy can be attributed to the presence of platinum, while gold can be considered responsible for structural stability. In addition, molecule dissociation is observed in the cases of H2, HCl, CH4, SO, and SO2 when Pt atoms are involved. Thus, two-dimensional Au9, Pt9, and Au18Pt18 show low adsorption energies against common molecules, reflecting adsorption energies observed in small Au and Pt clusters. These results demonstrate that Au18Pt18 can successfully utilize the low adsorption energies associated with platinum while preserving the integrity of the surface structure using gold atoms, making it possible to adsorb desired molecules using select areas of the Au18Pt18 surface.

Redesigning the Materials and Catalysts Database Construction Process Using Ontologies

Materials and catalyst informatics are emerging fields that are a result of shifts in terms of how materials and catalysts are discovered in the fields of materials science and catalysis. However, these fields are not reaching their full potential due to issues related to database creation and curation. Issues such as lack of uniformity, data selectivity, and the presence of bias affect the quality and usefuless of materials databases, especially when attempting to search for materials descriptors. Without uniform rules and frameworks, databases are limited in use outside of the intent of the creators of the database. Ontologies are therefore investigated as a means of redesigning the way materials and catalysts databases are designed and created. In particular, an ontology consisting of information found within the periodic table as well as commonly used related data is constructed and applied toward the search for materials descriptors. Additional ontologies are also developed for two databases-a database consisting of computational data related to perovskites and a database consisting of experimental data related to oxidative coupling of methane (OCM) catalysts-in order to investigate the impact of merging ontologies.

Prediction of the dopant activity of chemical compounds against ammonia borane with key descriptors: electronegativity and crystal structures

Key descriptors representing the desorption temperature of ammonia borane against hydrides and chlorides are investigated and applied to machine learning. The desorption temperature of ammonia borane with hydrides and chlorides is experimentally measured and a database is constructed. The database indicates a correlation between the desorption temperature and electronegativity. A support vector machine with selected descriptors from the database reveals the optimal amounts of CuCl2 and AgCl needed for decreasing the desorption temperature of ammonia borane. Prediction of the electronegativity that would achieve the desorption temperature is also revealed where the crystal structure is also found to be a key descriptor. Thus, electronegativity and crystal structures are revealed to be key descriptors that predict the desorption temperature of ammonia borane with a chemical compound. This would essentially lead towards finding the optimal amount of dopants and ideal dopants with a minimum number of experiments.