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From Evaluating The Performance Of Approximations In Density Functional Theory To A Machine Learning Design, Pedram Tavazohi
From Evaluating The Performance Of Approximations In Density Functional Theory To A Machine Learning Design, Pedram Tavazohi
Graduate Theses, Dissertations, and Problem Reports
Density-functional theory (DFT) has gained popularity because of its ability to predict the properties of a large group of materials a priori. Even though DFT is exact, there are inaccuracies introduced into the theory due to the approximations in the exchange-correlation (XC) functionals. Over the 50 years of its existence, scientists have tried to improve the design of the XC functionals. The errors introduced by these functionals are not consistent across all types of solid-state materials. In this project, a high throughput framework was utilized to compare the theoretical DFT predictions with the experimental results available in the Inorganic Crystal …