3 papers
nucl-th2020
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz +3
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of par…
nucl-th2020
Calibration of Energy Density Functionals with Deformed Nuclei
N. Schunck, J. O'Neal, M. Grosskopf +2
Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy…
cond-mat.str-el2018
Disorder induced power-law gaps in an insulator-metal Mott transition
Zhenyu Wang, Yoshinori Okada, Jared O'Neal +11
A correlated material in the vicinity of an insulator-metal transition (IMT) exhibits rich phenomenology and variety of interesting phases. A common avenue to induce IMTs in Mott i…