Prof. Farnaz Shakib, Assistant Professor of Chemistry & Environmental Science, New Jersey Institute of Technology

https://people.njit.edu/profile/shakib

Bio: Dr. Shakib completed her Bachelor degree in applied chemistry at the University of Tabriz, Iran, followed by her Master in computational organic chemistry at Tarbiat Modares University. She obtained her PhD from University of Alberta, Canada, with Prof. Gabriel Hanna in 2016. Her PhD studies was the starting point of her career in developing state-of-the-art nonadiabatic dynamics methods to study the quantum mechanical nature of charge transfer reactions. There, she developed and applied a new mixed quantum-classical Liouville method to simulate the dynamics of proton-coupled electron transfer (PCET) reactions in condensed phases. In March 2016 she entered the United States to continue her career as a postdoctoral research associate at the University of Rochester where she contributed to the development of methods such as Ring Polymer Surface Hopping and Quasi-Diabatic Integration Scheme to study thermal and photoinduced PCET reactions. In September 2019, Dr. Shakib joined NJIT to embark her independent research career with focus on developing accurate yet efficient computational platforms to investigate charge transfer dynamics in multi-configurational condensed-phase materials for energy storage/conversion purposes.

Title: "Two-dimensional electrically conductive metal-organic frameworks: Challenges and opportunities"

Abstract: 2-Dimensional (2D) metal-organic frameworks (MOFs) are a new class of multifunctional low-dimensional materials where extended layers of tetra-coordinated metal nodes with electron-rich π-conjugated organic linkers are stacked via van der Waals interactions. With two possible electron transport pathways along the intra- and inter-layer directions, 2D MOFs offer electrical conductivity on top of other known properties of MOFs, which include permanent porosity and exceptionally high surface area, promising unprecedented breakthroughs in producing high-performance and cost-effective materials for batteries, semiconductors, and supercapacitors. To make progress toward these applications, theoretical and computational tools have been utilized to unravel structure−property relationships, identify frameworks with tailored electronic properties, and develop design criteria for novel 2D MOFs yet to be experimentally synthesized and characterized. However, such studies are still in their infancy, hampered by various factors, including the high computational cost of simulating these complex extended materials composed of hundreds of atoms. In this talk, I will summarize and discuss our group’s efforts in mapping out the structure-property-function relationship of 2D MOFs while also deliberating utilization of machine learning (ML) techniques in property prediction of these electrically conductive (EC) materials. I will introduce our EC-MOF Database, the only database dedicated to 2D MOFs, which provides not only the crystal structure information but also the electronic properties of 1,072 structures, calculated at the periodic Density Functional Theory (DFT) level. I will also introduce the flexible nature of 2D MOFs and discuss how we employ molecular dynamics simulations based on our developed high-dimensional neural network potentials (NNPs) for capturing this flexibility.