1.6k citations · 7.9k across the 92 of their papers we have counts for
93 papers
Atomistic modeling of molecular beam epitaxy growth of SrTiO3 and Sr2TiO4 thin films
Guangfu Luo, Dane Morgan
Molecular beam epitaxy (MBE) is renowned for its potential for atomic layer control, but unexpected growth mechanisms can potentially compromise this level of precision. In this st…
Experimentally informed structure optimization of amorphous TiO2 films grown by atomic layer deposition
Jun Meng, Mehrdad Abbasi, Yutao Dong +4
Amorphous titanium dioxide TiO2 (a-TiO2) has been widely studied, particularly as a protective coating layer on semiconductors to prevent corrosion and promote electron-hole conduc…
Computational Discovery of Fast Interstitial Oxygen Conductors
Jun Meng, Md Sariful Sheikh, Ryan Jacobs +4
New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, e.g., below 400 Celsius. Interstit…
Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features
Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz +9
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model wa…
Thermophysical properties of FLiBe using moment tensor potentials
Siamak Attarian, Dane Morgan, Izabela Szlufarska
Fluoride salts are prospective materials for applications in some next generation nuclear reactors and their thermophysical properties at various conditions are of interest. Experi…
Machine learning in nuclear materials research
Dane Morgan, Ghanshyam Pilania, Adrien Couet +3
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature grad…