Dr. Faranak Hatami , Computational Chemistry , PhD at University of massachuessetes Lowell, United States
Faranak Hatami (Fara) is a dedicated physicist and researcher specializing in molecular dynamics simulations, machine learning, and nuclear materials science. Currently pursuing her Ph.D. in Physics at the University of Massachusetts Lowell, she focuses on transport property analysis and multi-objective optimization for molecular systems like Tri-Butyl-Phosphate (TBP). Faranak holds two master’s degrees—one in Physics from UMASS Lowell, where she explored force fields for TBP, and another in Nuclear Engineering from Shahid Beheshti University, where she investigated radiation damage in metals. With a robust background in computational physics, AI, and advanced simulation tools, she has authored multiple publications across nuclear materials and computational chemistry. Her teaching experience spans both the U.S. and Iran, reflecting her passion for education. Beyond academia, she completed a research internship at the University of Montreal. Faranak’s work bridges fundamental physics and practical applications, contributing innovative insights to the fields of material science and chemical engineering.
Professional Profile :
Google Scholar
Summary of Suitability for Award:
Faranak Hatami is a highly suitable candidate for a “Best Researcher Award”. She demonstrates exceptional multidisciplinary expertise spanning physics, molecular dynamics, machine learning, and nuclear materials science. Her Ph.D. work at UMASS Lowell innovatively combines atomic-scale simulations with AI to optimize force field parameters for Tri-Butyl-Phosphate, addressing both fundamental science and practical applications. She has authored several impactful publications in reputable journals and preprints, covering diverse topics from radiation damage in metals to machine learning models predicting thermodynamic properties. Her research portfolio includes complex computational modeling, multi-objective optimization, and advanced materials analysis. Additionally, Faranak’s teaching record and successful research internship in Canada reflect her commitment to knowledge dissemination and international collaboration. Her ability to merge computational physics with machine learning showcases originality and forward-thinking, key attributes for top research honors. Faranak Hatami embodies the qualities of a best researcher: scientific rigor, innovative thinking, multidisciplinary skillset, and impactful publications. Her contributions significantly advance computational methods in physical sciences and engineering, making her a strong and deserving candidate for a “Best Researcher Award”.
🎓Education:
Faranak Hatami is completing her Ph.D. in Physics at the University of Massachusetts Lowell (2021–2025), with her thesis focused on transport property analysis and optimization of force field parameters for Tri-Butyl-Phosphate (TBP), combining atomic-scale simulations with machine learning. Prior to this, she earned her M.Sc. in Physics from the same university in 2023, where she conducted a comparative study of force fields for liquid TBP using molecular dynamics. Earlier, she obtained her M.Sc. in Nuclear Engineering from Shahid Beheshti University in Iran (2016), where she examined radiation damage effects on zirconium and iron grain boundaries through simulations. Her academic journey began with a B.S. in Electrical Engineering from Kurdistan University in 2013. Throughout her studies, Faranak has integrated advanced computational methods, AI, and experimental data analysis, building a multidisciplinary foundation that connects physics, materials science, and engineering disciplines.
🏢Work Experience:
Faranak Hatami brings diverse experience across research, teaching, and technical projects. At UMASS Lowell, she serves as a Teaching Assistant in Physics while pursuing her Ph.D., guiding students through complex concepts. Previously, she lectured on Computational Methods and Statistical Methods and Physics courses at Shahid Beheshti University between 2014 and 2018. Her research career includes an internship at the University of Montreal (2019–2021), exploring hydrogen’s effects on iron grain boundaries using the kinetic activation relaxation technique (k-ART). Faranak has led significant academic projects spanning molecular dynamics simulations, multi-objective optimization, and machine learning applications in material science. She has deep expertise in computational tools such as LAMMPS, MCNP, VASP, and Python-based AI frameworks. Her work reflects a unique blend of fundamental physics research, practical problem-solving, and advanced data analysis, contributing to fields like chemical engineering, nuclear materials, and computational modeling.
🏅Awards:
Faranak Hatami has built an impressive research portfolio during her academic career, reflected in multiple publications and conference presentations. While specific named awards were not explicitly listed in her profile, her contributions have earned her recognition through invited presentations such as at the AIChE Annual Meeting, showcasing her expertise in molecular dynamics simulations and force field optimization. Completing dual M.Sc. degrees in Physics and Nuclear Engineering highlights her dedication and academic excellence. Her selection as a research intern at the University of Montreal, working on advanced computational studies in materials science, further underscores her capability and esteem in her field. Through her multidisciplinary approach integrating AI, molecular modeling, and nuclear materials science, she stands out as a rising scholar contributing valuable insights to computational physics and chemical engineering. As she advances her Ph.D., she is poised for further accolades in research innovation and scientific community engagement.
🔬Research Focus:
Faranak Hatami focuses her research on the intersection of molecular dynamics simulations, machine learning, and materials science. Her Ph.D. work centers on analyzing transport properties and optimizing force field parameters for Tri-Butyl-Phosphate (TBP) using multi-objective optimization algorithms like NSGA-II/III. She applies molecular dynamics to predict critical thermodynamic and transport properties, integrating neural networks for parameter tuning. Additionally, she explores AI-based classification of microscopy and atomic-scale images, blending physics with cutting-edge data science. Faranak’s earlier research in nuclear engineering examined radiation damage in metals such as zirconium and nickel, utilizing techniques like climbing image nudged elastic band (CI-NEB) for defect analysis. She’s also investigated hydration free energies, grain boundary behaviors, and primary knock-on atom (PKA) spectra in irradiated materials. Her work bridges computational physics with practical engineering challenges, advancing predictive models and simulation methods to better understand complex molecular and material systems.
Publication Top Notes:
✅ Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data
Citations: 6
✅ Quantification of Methane Hydration Energy Through Free Energy Perturbation Method
✅ Comparison of Different Machine Learning Approaches to Predict Viscosity of Tri-n-Butyl Phosphate Mixtures Using Experimental Data
Citations: 3
✅ Properties of Tri-Butyl-Phosphate from Polarizable Force Field MD Simulations
Citations: 1
✅ A Revision of Classical Force Fields for Tri-N-Butyl Phosphate Molecular Dynamics Simulations
✅ Interaction of primary cascades with different atomic grain boundaries in α-Zr: An atomic scale study
Citations: 34
✅ An energetic and kinetic investigation of the role of different atomic grain boundaries in healing radiation damage in nickel
Citations: 31