Assistant Professor, Physics, Applied Physics & Astronomy
Trevor David Rhone's research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search new 2D materials with exotic properties, the prediction of the outcome of industrially relevant catalytic reactions or for other compelling research problems.
Xie, Y., Tritsaris, G.A., Grånäs, O., Rhone, T.D. (2021) Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials, Journal of Physical Chemistry Letters, 12, pp.12048-12054.
Defo, R.K., Nguyen, H., Ku, M.J.H., Rhone, T.D. (2021) Methods to accelerate high-throughput screening of atomic qubit candidates in van der Waals materials, Journal of Applied Physics, 129, 2021.
Rhone, T.D., Chen, W., Desai, S. Torrisi, S.B., Larson, D.T., Yacoby, A., Kaxiras, E.(2020) Data-driven studies of magnetic two-dimensional materials, Scientific Reports, 10, 2020.
Zhu, Y., Kong, X., Rhone, T.D., Guo, H. (2018) Systematic search for two-dimensional ferromagnetic materials, Physical Review Materials, 2
Ueno, T., Rhone, T.D., Hou, Z., Mizoguchi, T., Tsuda, K. (2016) COMBO: An efficient Bayesian optimization library for materials science, Materials Discovery, 4, pp.18-21.