Dr. Faranak Hatami | Computational Chemistry | Best Researcher Award

Published on by Chemistry Awards

Dr. Faranak Hatami | Computational Chemistry | Best Researcher Award

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

Dr. Miguel Fernández | Computational Chemistry | Best Researcher Award

Published on by Chemistry Awards

Dr. Miguel Fernández | Computational Chemistry | Best Researcher Award

Dr. Miguel Fernández , Instituto Venezolano de Investigaciones Cientificas IVIC , Venezuela

Dr. Miguel Antonio Fernández Castellanos is a Venezuelan computational chemist with expertise in molecular modeling and theoretical chemistry. Based in San Antonio de los Altos, Venezuela, he is a researcher at the Laboratory of Computational Chemistry at IVIC, where he models the antioxidant effects of magnesium salts and maintains HPC servers. With a Ph.D. in Chemistry from IVIC, Dr. Fernández has made notable contributions to understanding molecular interactions through computational simulations. He has authored numerous peer-reviewed publications and presented at national and international conferences. Dr. Fernández is the recipient of prestigious awards, including the Dr. Arnoldo Gabaldón Award and the National Science, Technology, and Innovation Award, recognizing his outstanding research in chemistry.

Professional Profile

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Summary of Suitability for Award:

Dr. Miguel Antonio Fernández Castellanos is an exceptional candidate for the “Best Researcher Award” due to his outstanding contributions to computational and theoretical chemistry. With a Ph.D. from IVIC, Dr. Fernández has authored numerous high-impact publications focusing on antioxidant mechanisms, lipid bilayer oxidation, and magnesium salt interactions. His work combines advanced molecular dynamics, electronic structure methods, and theoretical modeling to address critical challenges in chemistry and biomedicine. Dr. Miguel Antonio Fernández Castellanos is a highly deserving candidate for the “Best Researcher Award.” His innovative contributions, recognized excellence, and impactful research addressing critical chemical and biomedical challenges solidify his place as an exemplary researcher whose work resonates globally.

🎓Education:

Dr. Fernández earned his Ph.D. in Chemistry from the Instituto Venezolano de Investigaciones Científicas (IVIC) in 2021, specializing in molecular dynamics and electronic structure methods. He holds a Bachelor’s degree in Chemistry from the Universidad de Carabobo (UC), completed in 2012. His academic background combines robust theoretical training with practical expertise in computational chemistry, focusing on the simulation of complex molecular systems and the study of antioxidant mechanisms of magnesium salts. His advanced technical skills include molecular dynamics software (ORCA, GROMACS) and programming languages such as Python and BASH, enabling him to address cutting-edge research questions in chemistry.

🏢Work Experience:

Dr. Fernández has a rich professional background in research and academia. He has been a researcher at IVIC’s Laboratory of Computational Chemistry since 2021, focusing on magnesium salts’ antioxidant effects. He completed a postdoctoral fellowship at IVIC (2021–2024) and previously worked as a Teaching Assistant at the Universidad de Carabobo, teaching General and Organic Chemistry and assisting in laboratory practices. His responsibilities include managing laboratory resources, maintaining HPC servers, and conducting advanced molecular modeling. Dr. Fernández has actively contributed to scientific knowledge dissemination through publications, conferences, and posters, showcasing his expertise in computational simulations and theoretical chemistry.

🏅Awards: 

Dr. Fernández’s accomplishments have been recognized with several prestigious awards. He received the Dr. Arnoldo Gabaldón Award (2023) from ACFIMAN for his contributions as a young scientist in Venezuela. In 2021, he won the National Science, Technology, and Innovation Award for his groundbreaking work on magnesium sulfate’s antioxidant effects. Additionally, he has been part of the Laboratory of Computational Chemistry team, which received the Consolidated Research Group Award. Dr. Fernández also earned the IVIC Excellence Scholarship for his doctoral studies, reflecting his dedication to advancing computational chemistry and his impact on scientific research in Venezuela.

🔬Research Focus:

Dr. Fernández’s research explores computational chemistry, focusing on molecular dynamics and antioxidant mechanisms of magnesium salts. He investigates spin-electron stabilization in hydroxyl radicals, lipid bilayer oxidation, and bioinorganic interactions using advanced DFT calculations. His studies contribute to understanding cellular processes and oxidative stress, bridging theoretical chemistry with biomedical applications. He also explores interfacial properties of surfactant systems and proton transfer phenomena in hydrated magnesium complexes. Dr. Fernández’s work employs state-of-the-art simulation tools, including ORCA and GROMACS, to advance theoretical insights into chemical and biological systems, addressing fundamental and applied challenges in chemistry.

Publication Top Notes:

Title: Relevance of SOMO-HOMO inversion in the antioxidant activity of MgSO4-OH radical complex: A theoretical insight
Authors: Miguel Fernández
Citations: Not available
Year: 2025

Title: Antioxidant Activity of MgSO4 Ion Pairs by Spin-Electron Stabilization of Hydroxyl Radicals through DFT Calculations: Biological Relevance
Authors: Miguel Fernández
Citations: Not available
Year: 2024

Title: A literature review of the increased intracellular free calcium concentration by biofield therapy or laser exposure. An explanation by using a theoretical study of hydrated calcium ions
Authors: Miguel Fernández
Citations: Not available
Year: 2024

Title: Comités de bioética: exponiendo los desafíos bioéticos actuales
Authors: Miguel Fernández
Citations: Not available
Year: 2023

Title: Magnesium salts in pregnancy
Authors: Miguel Fernández
Citations: Not available
Year: 2023

Title: Effect of magnesium sulfate in oxidized lipid bilayers properties by using molecular dynamics
Authors: Miguel Fernández
Citations: Not available
Year: 2021

Title: Deslocalización de la densidad de espín del radical hidroxilo sobre el MgSO4. Un estudio teórico
Authors: Miguel Fernández
Citations: Not available
Year: 2019

Title: Hydration study of MgSO4 using different theoretical and model approaches: ¿Is there a proton transfer?
Authors: Miguel Fernández
Citations: Not available
Year: 2018

Title: Magnesium sulfate against oxidative damage of membrane lipids: A theoretical model
Authors: Miguel Fernández
Citations: Not available
Year: 2017

Title: Análisis teórico de la fluorescencia de emisión de metalo-tetrafenil-porfirinas usando teoría del funcional de la densidad dependiente del tiempo
Authors: Miguel Fernández
Citations: Not available
Year: 2014