activity
20242026
most citedMachine learning interatomic potential for predicting the thermal properties of uranium nitride

5 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci2026

Electric-field effects on defect migration energetics in GaN

Farshid Reza, Hamdy Arkoub, Alexander S. Hauck +2

A predictive understanding of defect transport in GaN under operating electric fields is critical for assessing device reliability in high-power and radiation environments. In this…

cond-mat.mtrl-sci2026

Thermal Transport in Defective Uranium Nitride: Effects of Point Defects, Anharmonicity, and Electronic Contributions

Beihan Chen, Marat Khafizov, Zilong Hua +2

The impact of point defects on thermal transport in uranium nitride (UN) is investigated using a MLIP combined with Green-Kubo (GK) and normal mode analysis (NMA) methods over 300-…

cond-mat.mtrl-sci20255 cited

Machine learning interatomic potential for predicting the thermal properties of uranium nitride

Beihan Chen, Zilong Hua, Jennifer K. Watkins +4

We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic…

cond-mat.mtrl-sci2025

Evaluation of Structural Properties and Defect Energetics in AlGaN Alloys

Farshid Reza, Beihan Chen, Miaomiao Jin

AlGaN alloys are essential for high-performance optoelectronic and power devices, yet the role of composition on defect energetics remains underexplored, largely due to…

cond-mat.mtrl-sci2024

Examining composition-dependent radiation response in AlGaN

Miaomiao Jin, Farshid Reza, Alexander Hauck +4

AlGaN materials have become increasingly important for electronics in radiation environments due to their robust properties. In this work, we aim to investigate the ato…