#  Emine Kucukbenli 

Affiliate, former Postdoctoral Fellow

 

 

 



   ![Emine Kucukbenli](/sites/g/files/omnuum11321/files/styles/hwp_4_5__320x400/public/efthimios_kaxiras/files/emine.jpg?itok=KTfvVXYa) 

 



 





 

Emine Kucukbenli's research aims to explore the vast landscape of crystal structures that atoms or molecules form. She builds numerical tools to speed up the exploration using machine learning \[1,2\] or to identify different points on this landscape that correspond to different material properties such as NMR or phonon spectrum \[3-7\]. Her work is mainly focused on numerical tool building rather than the applications themselves, but sometimes she stumbles upon mysteries of elusive crystals \[8\] or puzzling experimental findings \[9\] that draw her curiosity. She supports open source Density Functional Theory software packages through development and enhancement of numerical methods \[10\] and she is a passionate advocate for reproducibility in science \[11,12\].

Emine Kucukbenli holds a BSc in Physics from Bilkent University, Turkey and PhD in Numerical and Theoretical Condensed Matter Physics from SISSA, Italy. Prior to her appointment at Harvard University, she was a postdoctoral researcher for E-CAM Centre of Excellence at SISSA and at Theos research group at EPFL, Switzerland. She was generously supported by non-governmental organizations such as ICTP, TWAS, AUST, OWSD/GenderInSITE during her work for and with under-represented communities in science as organizer and participant of workshops, lecture series, conference panels and outreach programs.

\[1\]R Lot, F Pellegrini, Y Shaidu, E Kucukbenli, "PANNA: Properties from Artificial Neural Network Architectures", in preparation (2019).

\[2\]F Pellegrini, R Lot, Y Shaidu, E Kucukbenli, "Reproducibility and transfer of knowledge in deep neural networks for electronic structure" in preparation (2019).

\[3\]DOI: 10.1021/jp3019974

\[4\]DOI: 10.1103/PhysRevB.84.235119

\[5\]DOI: 10.1016/j.chemphys.2013.06.024

\[6\]DOI: 10.1088/0953-8984/24/42/424209

\[7\]DOI: 10.1103/PhysRevB.93.235120

\[8\]DOI: 10.1107/S205225251701096X

\[9\]DOI: 10.1021/acs.jpcc.8b05689 ,

\[10\]DOI: 10.1088/1361-648x/aa8f79

\[11\]DOI: arXiv:1404.3015

\[12\]DOI: 10.1126/science.aad3000



 

 

 





 

 

- ## Status
    
     [Alumni](/member-status/alumni)
- ## Members
    
     [Affiliate](/members/affiliate)
- ## Topic
    
     [Machine Learning Applications](/topic/machine-learning-materials)