University of Massachusetts Lowell - United States
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Early Academic Pursuits
Dr. Faranak Hatami’s academic journey reflects a multidisciplinary pursuit of knowledge rooted in Computational theory, physics, and engineering. Beginning with a B.S. in Electrical Engineering from Kurdistan University in 2013, she demonstrated early interest in both the physical sciences and applied computation. She further advanced her academic credentials with an M.Sc. in Nuclear Engineering from Shahid Beheshti University (SBU) in 2016, focusing on the effects of radiation damage on materials through molecular dynamics simulations. Her keen focus on atomistic modeling led her to pursue dual graduate degrees at the University of Massachusetts, Lowell an M.Sc. (2021–2023) and a Ph.D. in Physics (expected May 2025) where she embraced modern methodologies, such as AI-driven optimization and atomic-scale simulations.
Professional Endeavors
Throughout her academic and research career, Dr. Hatami has bridged theoretical physics and practical computational applications, especially within the realm of Computational theory. Her professional experience spans multiple roles as a teaching assistant and lecturer, including courses on computational and statistical methods, general physics, and medical device physics. At Shahid Beheshti University, she served as a lecturer from 2016 to 2018 and currently continues as a teaching assistant in Physics at the University of Massachusetts, Lowell. Her academic projects are not only aligned with modern research priorities but are also deeply informed by her interdisciplinary training in physics, nuclear engineering, and machine learning.
Contributions and Research Focus
Dr. Hatami's research has made notable contributions to the fields of Molecular Dynamics, force field optimization, and Computational theory. Her Ph.D. thesis focuses on the Transport Property Analysis and Multi-Objective Optimization of Force Field Parameters for Tri-Butyl-Phosphate (TBP) a novel integration of atomistic simulations with machine learning techniques like NSGA-II, NSGA-III, and neural networks. Her research explores dynamic properties (diffusion, viscosity) and thermodynamic characteristics of liquid molecules, with direct applications in nuclear and chemical process industries. Additionally, she has developed and compared classical and machine learning models for predicting physical properties such as viscosity using Python and high-performance computing tools.
Impact and Influence
Dr. Hatami’s work stands at the intersection of physics, materials science, and Computational theory, bringing together AI and molecular simulations in an innovative manner. Her projects ranging from image-based classification of biomolecular condensates using CNNs to free energy calculations and radiation damage analysis highlight her ability to apply computational models to a broad spectrum of physical problems. These efforts place her among a growing cohort of researchers leading the transformation of classical physics problems through machine learning and data-driven methods. Her influence is expanding through her contributions to academic instruction, research dissemination, and interdisciplinary collaboration.
Academic Cites
While still completing her Ph.D., Dr. Hatami’s work has already gained academic traction. Her publications on molecular simulations and machine learning applications in physical chemistry are being cited for their originality and methodological rigor. Her comparative analysis of classical and AI-based models and the use of multi-objective optimization in force field development are expected to become foundational references for future researchers working at the intersection of Modeling and Computational theory.
Legacy and Future Contributions
Dr. Faranak Hatami is poised to be a leading figure in the integration of Computational theory with atomic-scale physics. Her future goals include expanding her work in multi-physics simulations, advancing ML-based predictive models, and fostering cross-disciplinary innovations in nuclear, materials, and biomedical engineering. Through continued publication, teaching, and collaboration, she is shaping a legacy of data-driven physics that not only redefines simulation methodologies but also contributes to real-world applications in energy, medicine, and materials science.
📘Computational theory
Dr. Hatami's research is deeply rooted in Computational theory, from optimizing molecular dynamics parameters to building neural networks for property prediction. Her implementation of Computational theory across physics, AI, and molecular modeling demonstrates how data and computation are revolutionizing modern science. As she continues her academic career, her work will remain central to advancements in Computational theory and its role in solving complex physical problems.
✍️ Notable Publication
📘Interaction of primary cascades with different atomic grain boundaries in α-Zr: An atomic scale study
Authors: F. Hatami, S.A.H. Feghhi, A. Arjhangmehr, A. Esfandiarpour
Journal: Journal of Nuclear Materials
Year: 2016
Citations: 34
📘An energetic and kinetic investigation of the role of different atomic grain boundaries in healing radiation damage in nickel
Authors: A. Arjhangmehr, S.A.H. Feghhi, A. Esfandiyarpour, F. Hatami
Journal: Journal of Materials Science
Year: 2016
Citations: 31
📘Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data
Authors: F. Hatami, M. Moradi
Journal: Computation
Year: 2024
Citations: 6
📘Comparison of Different Machine Learning Approaches to Predict Viscosity of Tri-n-Butyl Phosphate Mixtures Using Experimental Data
Authors: F. Hatami, M. Moradi
Year: 2023
Citations: 3
📘Properties of Tri-Butyl-Phosphate from Polarizable Force Field MD Simulations
Authors: F. Hatami, V. de Almeida
Conference: 2022 AIChE Annual Meeting
Year: 2022
Citations: 1