activity
20172021
most citedAccurate Force Field for Molybdenum by Machine Learning Large Materials Data

165 citations · 325 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci202113 cited

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused X-rays

Ivan A. Zaluzhnyy, Peter O. Sprau, Richard Tran +17

We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO-based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-f…

cond-mat.str-el20201 cited

The Breakdown of Mott Physics at VO Surfaces

Matthew J. Wahila, Nicholas F. Quackenbush, Jerzy T. Sadowski +12

Transition metal oxides such as vanadium dioxide (VO), niobium dioxide (NbO), and titanium sesquioxide (TiO) are known to undergo a temperature-dependent metal-insu…

cond-mat.mtrl-sci20192 cited

Grain Boundary Properties of Elemental Metals

Hui Zheng, Xiang-Guo Li, Richard Tran +5

The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional…

cond-mat.mtrl-sci2019144 cited

Anisotropic work function of elemental crystals

Richard Tran, Xiang-Guo Li, Joseph Montoya +3

The work function is a fundamental electronic property of a solid that varies with the facets of a crystalline surface. It is a crucial parameter in spectroscopy as well as materia…

physics.comp-ph2017165 cited

Accurate Force Field for Molybdenum by Machine Learning Large Materials Data

Chi Chen, Zhi Deng, Richard Tran +3

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning tech…