In our research, we use computation, modeling, and simulation to gain insight into fundamental problems at the intersection of materials science, mechanics, and condensed matter physics. We use modeling and simulation to develop predictive relationships between structure and function, in order to enable materials discovery, design, and optimization using methods such as high-throughput computation, materials informatics, and inverse materials design. Further insight into our computational results is obtained through regular collaborations with experimental groups through sustained, closed-loop feedback and interaction. We are especially interested in obtaining understanding and control over the microscopic processes in materials that enable energy conversion and storage, thermal properties, transport in the solid state, and unique behaviors in low-dimensional/nanostructured systems. On the applications side, our current interests are in battery materials, thermoelectrics, solid oxide fuel and electrolysis cells, and wide band gap semiconductors for power electronics. On the methodological side, we are pursuing the development of data science/machine learning approaches rooted in probabilistic modeling and designed from the outset for the materials domain.

Our group is dedicated to providing a welcoming, respectful, and inclusive environment for everyone.  We value strong relationships, mentoring, close collaboration and perspectives from diverse backgrounds as we work towards our goals in creating a  sustainable and healthy future through insights gained by carrying out robust, transparent science. 

Modeling magneto-structural phase transformations

We are developing predictive models of the coupled magnetic and structural phase transformations in magnetic shape memory alloys.

Modeling disordered systems

We aim to develop models to investigate intrinsic and extrinsic defects in disordered energy materials to optimize performance.

Machine learning-guided materials design

We are developing few-shot and generative machine learning methods to handle data scarcity and accelerate materials design.

2023

• Adrian Xiao Bin Yong, Lawrence Anderson, Nicola H. Perry, Elif Ertekin. "Effects of State Filling and Localization on Chemical Expansion in Praseodymium-Oxide Perovskites", J. Mater. Chem. A 11 4045 (2023). DOI: 10.1039/D2TA06756K



• Kamil M. Ciesielski, Brenden R. Ortiz, Lidia C. Gomes, Vanessa Meschke, Jesse M. Adamczyk, Tara L. Braden, Dariusz Kaczorowski, Elif Ertekin, Eric S. Toberer. "Strong Scattering from Low-Frequency Rattling Modes Results in Low Thermal Conductivity in Antimonide Clathrate Compounds", Chem. Mater. 35 2918 (2023). DOI: 10.1021/acs.chemmater.2c03821



• Michael Y. Toriyama, Jiaxing Qu, Lidia C. Gomes, Elif Ertekin. "VTAnDeM: A Python Toolkit for Simultaneously Visualizing Phase Stability, Defect Energetics, and Carrier Concentrations of Materials", Computer Phys. Commun 287 108691 (2023). DOI: 10.1016/j.cpc.2023.108691



• Channyung Lee, Nathan D. Rock, Ariful Islam, Michael A. Scarpulla, Elif Ertekin. "Electron-phonon effects and temperature-dependence of the electronic structure of monoclinic beta-Ga2O3", APL Materials 11 11106 (2023). DOI: 10.1063/5.0131453



• Nicholas Weadock, Tyler Sterling, Julian Vigil, Aryeh Gold-Parker, Ian Smith, Ballal Ahammed, Matthew Krogstad, Feng Ye, David Voneshen, Peter Gehring, Andrew M. Rappe, Hans-Georg Steinruck, Elif Ertekin, Hemamala Karunadasa, Dmitry Reznik, Michael Toney. "The nature of dynamic local order in CH₃NH₃PbI₃ and CH₃NH₃PbBr₃", Joule 7 1051 (2023). DOI: 10.1016/j.joule.2023.03.017

View all publications here.

How to contact Elif:

E-mail is your best bet, even for those students who are here on campus already. I will do my best to respond to you within a reasonable time frame.