Dr Nastaran Meftahi | ARC Centre of Excellence in Exciton Science

Dr Nastaran Meftahi

Research Fellow

Nastaran has extensive experience with computational methods such as machine-learning techniques and molecular dynamic simulations. Her research focuses on applying advanced linear and nonlinear machine learning techniques such as multiple linear regression and artificial neural networks to explore the relationship between structure and photo-luminescent properties for photovoltaic materials. She also uses classical molecular dynamics simulations and ab initio techniques to examine the mutual orientation and properties of organic photo-dyes in solar cell matrices. 

Qualifications: 
PhD Chemistry, La Trobe University, Australia (2018)
MSc Chemistry, K.N.Toosi University of Technology, Iran (2012)
BSc Chemistry, Mazandaran University, Iran (2008)

Publications

Journal Articles
Elbourne, A.; Besford, Q. A.; Meftahi, N.; Crawford, R. J.; Daeneke, T.; Greaves, T. L.; McConville, C. F.; Bryant, G.; Bryant, S. J.; Christofferson, A. J. The Impact of Water on the Lateral Nanostructure of a Deep Eutectic Solvent–Solid Interface. Australian Journal of Chemistry 2022, 75 (2), 111-125 DOI: 10.1071/CH21078. doi: 10.1071/CH21078
Meftahi, N.; Walker, M. L.; Smith, B. J. Predicting aqueous solubility by QSPR modeling. Journal of Molecular Graphics and Modelling 2021, 106, 107901 DOI: 10.1016/j.jmgm.2021.107901. doi: 10.1016/j.jmgm.2021.107901
Pathirannahalage, S. P. Kadaolu; Meftahi, N.; Elbourne, A.; Weiss, A. C. G.; McConville, C. F.; Padua, A.; Winkler, D. A.; Gomes, M. Costa; Greaves, T. L.; Le, T. C.; et al. Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations. Journal of Chemical Information and Modeling 2021, 61 (9), 4521 - 4536 DOI: 10.1021/acs.jcim.1c00794. doi: 10.1021/acs.jcim.1c00794
Ghasemian, M. B.; Zavabeti, A.; Mousavi, M.; Murdoch, B. J.; Christofferson, A. J.; Meftahi, N.; Tang, J.; Han, J.; Jalili, R.; Allioux, F. ‐M.; et al. Doping Process of 2D Materials Based on the Selective Migration of Dopants to the Interface of Liquid Metals. Advanced Materials 2021, 33 (43), 2104793 DOI: 10.1002/adma.202104793. doi: 10.1002/adma.202104793
Tang, J.; Lambie, S.; Meftahi, N.; Christofferson, A. J.; Yang, J.; Ghasemian, M. B.; Han, J.; Allioux, F. - M.; Rahim, M. Arifur; Mayyas, M.; et al. Unique surface patterns emerging during solidification of liquid metal alloys. Nature Nanotechnology 2021, 16 (4), 431 - 439 DOI: 10.1038/s41565-020-00835-7. doi: 10.1038/s41565-020-00835-7
Elbourne, A.; Meftahi, N.; Greaves, T. L.; McConville, C. F.; Bryant, G.; Bryant, S. J.; Christofferson, A. J. Nanostructure of a deep eutectic solvent at solid interfaces. Journal of Colloid and Interface Science 2021, 591, 38 - 51 DOI: 10.1016/j.jcis.2021.01.089. doi: 10.1016/j.jcis.2021.01.089
Meftahi, N.; Klymenko, M. V.; Christofferson, A. J.; Bach, U.; Winkler, D. A.; Russo, S. P. Machine learning property prediction for organic photovoltaic devices. npj Computational Materials 2020, 6 (1) DOI: 10.1038/s41524-020-00429-w. doi: 10.1038/s41524-020-00429-w
Meftahi, N.; Manian, A.; Christofferson, A. J.; Lyskov, I.; Russo, S. P. A computational exploration of aggregation-induced excitonic quenching mechanisms for perylene diimide chromophores. The Journal of Chemical Physics 2020, 153 (6), 064108 DOI: 10.1063/5.0013634. doi: 10.1063/5.0013634
Meftahi, N.; Klymenko, M. V.; Christofferson, A. J.; Bach, U.; Winkler, D. A.; Russo, S. P. Machine learning property prediction for organic photovoltaic devicesAbstract. npj Computational Materials 2020, 6 (1) DOI: 10.1038/s41524-020-00429-w. doi: 10.1038/s41524-020-00429-w