Principal Investigator
Curriculum Vitae (CV)			Xiaofeng Qian, Ph.D. Assistant Professor Department of Materials Science and Engineering College of Engineering and College of Science Texas A&M University College Station, TX 77843-3003 B.S., Engineering Physics, Tsinghua University, China, 2001 Ph.D., Nuclear Science and Engineering, MIT, 2008   - Dissertation: Electronic Structure and Transport in Molecular and Nanoscale Electronics   - Supervisor: Sidney Yip Postdoc. at Nicola Marzari's group, Department of Materials Science and Engineering, MIT (2008-2011) Postdoc. at Ju Li's group, Department of Nuclear Science and Engineering, MIT (2011-2014) Email:  Phone: +1-979-458-9843 Fax:     +1-979-862-6835 Mailing Address: 3003 TAMU, College Station, TX 77843-3003 Office Address: 224 RDMC, 575 Ross St, College Station, TX 77843

			Graduate Students
			Baiyu Zhang, B.S. Graduate Student, Materials Science and Engineering (2016 - Present) First-Principles Study of Electronic Structure and Defect Physics in Photovoltaic and Neuromorphic Materials First-principles study of electronic structure and defect physics of antimony chalcogenides Interface engineering and defect control for improving solar cell efficiency Theoretical studies of correlated oxides, 2D materials, and ferroelectrics for neuromorphic computing
			Daniel Willhelm, B.S. Graduate Student, Materials Science and Engineering (2017 - Present) Theoretical Investigations of Van der Waals 2D Heterostructures Using Machine Learning and Density-Functional Theory First-principles investigations of van der Waals heterostructures Machine learning enabled van der Waals materials discovery Interpretable machine learning models of electronic structure
			Nathan Wilson, B.S. Graduate Student, Materials Science and Engineering (2017 - Present) Theory-Guided Optimal Materials Discovery and Design First-principles calculations of thermoelectric properties Active machine learning for accelerated and convergent classical force field development Advanced feature engineering and physics guided machine learning for materials science
			Xiuyu Tang, B.S. Graduate Student, Materials Science and Engineering (2019 - Present) Nonlinear Light-Matter Interaction and Non-equilibrium Matter Topological materials Moiré pattern
			Jaybelle Dannah Pranada, B.S. Graduate Student, Materials Science and Engineering (2019 - Present) Theoretical Study of Extrinsic Doping in Antimony Chalcogenide Solar Cells Density functional theory calculations of antimony chalcogenides Defect physics upon extrinsic doping
			Alex Strasser Graduate Student, Materials Science and Engineering (2020 - Present) First-Principles Study of Nonlinear Optical Materials Electronic structure theory Linear and nonlinear optical responses

			Undergraduate Students
			Daniel Peralta Velazquez Undergraduate Student, Computer Engineering (2019 - Present) Database Implementation and Machine Learning Applications for Materials Science Database implementation Data analysis with machine learning
			Ashen Kenna Undergraduate Student, Materials Science and Engineering (2020 - Present) Electronic and Optical Properties of One-Dimensional Materials
			Kaleigh Curtis Undergraduate Student, Materials Science and Engineering (2020 - Present) Low-Dimensional Piezoelectric Materials

			Alumni
			Hua Wang, Ph.D. Graduate Student (PhD, 2015-2020) Current Affiliation: Postdoctoral Associate, MIT 2020 Association of Former Students Distinguished Graduate Student Award, Texas A&M University 2020 MSEN Best PhD Thesis Award, Texas A&M University First-Principles Theory, Discovery, and Design of Nonlinear Optical Materials First-principles theory and method development of nonlinear optical responses Second harmonics generation and nonlinear photocurrent in 2D materials and topological materials Ferroicity-driven nonlinear photocurrent photocurrent switching and nonlinear anomalous Hall effect Discovery of 2D ferroelectrics, ferromagnetics, and multiferroics
			Dana Ronin Undergraduate Student, Bioengineering, University of Maryland NSF REU student, Summer 2019 Modeling 2D Multiferroic GeSe Using Machine Learning Training and benchmarking machine learning force field for monolayer GeSe Molecular dynamics simulations of monolayer GeSe using
			Yang Yang, M.S. Visiting Student, Xi'an Jiaotong University (2017-2019) Machine Learning Force Field Development of machine learning force field Sampling and training of machine learning force field for 2D materials Molecular dynamics simulations of phase transition and deformation

Copyright © 2020 Xiaofeng Qian.