Dr. Faranak Hatami | Computational Chemistry | Best Researcher Award

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 : 

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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. Peng Wang | Computational Modeling | Best Researcher Award

Dr. Peng Wang | Computational Modeling | Best Researcher Award

Dr. Peng Wang | Computational Modeling | Lecturer at School of Mining and Coal Engineering, Inner Mongolia University of Science and Technology, China

Peng Wang is a Lecturer at the School of Mining and Coal Engineering, Inner Mongolia University of Science and Technology. His research primarily focuses on mine safety, coal spontaneous combustion mechanisms, and numerical simulation techniques. With a strong academic background, he has led innovative studies on coal gangue heap oxidation and spontaneous combustion hazard zones, contributing significantly to the field of mining safety. Over the past three years, he has published 9 academic papers as first or corresponding author, with 7 indexed in SCI/EI journals. He has successfully led two research projects, including the prestigious Inner Mongolia Autonomous Region Doctoral Research Innovation Grant. Additionally, he has played a key role as a technical lead in three industry projects. His contributions to coal safety and emergency management were recognized in 2024 when he was awarded the First Prize for Outstanding Paper at the Safety and Emergency Management Talent Development Conference.

Professional Profile :         

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Scopus 

Summary of Suitability for Award:

Peng Wang is a highly qualified candidate for the “Best Researcher Award” due to his significant contributions to coal spontaneous combustion mechanisms and mine safety. With over 15 research publications, including 7 SCI/EI-indexed papers as the first or corresponding author, he has demonstrated strong research expertise. He has successfully led two research projects, including the Inner Mongolia Autonomous Region Doctoral Research Innovation Grant, and has been a technical lead in three major projects. His interdisciplinary work, combining numerical simulation and experimental analysis, provides groundbreaking insights into fire prevention in mining. His contributions have been recognized with the First Prize for Outstanding Paper at the Safety and Emergency Management Talent Development Conference (2024). Considering Peng Wang’s research impact, publications, awards, and leadership in industry collaborations, he is a highly suitable candidate for the “Best Researcher Award” in the field of Mine Safety and Coal Combustion Prevention.

🎓Education:

Peng Wang holds a strong academic background in mining and coal engineering. He completed his doctoral studies in mine safety, focusing on the spontaneous combustion of coal gangue heaps and numerical simulations for risk mitigation. His research has been instrumental in developing new strategies for preventing coal-related hazards in dry and windy mining regions. His Ph.D. research was supported by the Inner Mongolia Autonomous Region Doctoral Research Innovation Grant, highlighting its significance in industrial safety applications. He pursued his graduate and undergraduate studies in mining engineering, equipping himself with deep technical expertise in coal combustion mechanisms and mineral-reagent interactions. His educational journey has laid a robust foundation for his research contributions in spontaneous combustion safety, mine ventilation, and fire prevention strategies. Through continuous academic pursuits, he has built a strong profile in applied mining safety and innovative coal research.

🏢Work Experience:

Peng Wang has extensive experience in the field of mining safety and coal combustion research. As a Lecturer at Inner Mongolia University of Science and Technology, he has been actively involved in both academic and industry projects. Over the years, he has led two major research projects, including one funded by the Inner Mongolia Autonomous Region. Additionally, he has contributed as a technical lead to three large-scale industry projects focused on mine safety and fire prevention. His expertise extends to numerical simulation methods, allowing him to model coal oxidation processes and spontaneous combustion risks effectively. His contributions as a member of the China Coal Society further demonstrate his active engagement in scientific communities. With a strong research output of 15 journal papers and participation in key national projects, he continues to advance the safety protocols and preventive measures in coal mining operations.

🏅Awards: 

Peng Wang has received several prestigious recognitions for his outstanding contributions to mining safety and coal spontaneous combustion research. In 2024, he was awarded the First Prize for Outstanding Paper at the Safety and Emergency Management Talent Development Conference for his significant work on coal gangue spontaneous combustion mechanisms. His research excellence was also acknowledged through the Inner Mongolia Autonomous Region Doctoral Research Innovation Grant, which supported his pioneering studies in mine fire prevention. As a major contributor to the National Natural Science Foundation of China, he has played a crucial role in advancing coal safety research at the national level. Additionally, he has served as a technical lead in three large-scale industry projects, focusing on risk mitigation in coal mining operations. His active membership in the China Coal Society further highlights his professional engagement in developing innovative strategies for fire prevention and mine safety improvements.

🔬Research Focus:

Peng Wang specializes in mine safety, with a strong emphasis on preventing coal spontaneous combustion and improving fire mitigation strategies. His research has led to groundbreaking studies on the oxidation and air seepage properties of coal gangue heaps, crucial for determining spontaneous combustion hazard zones. By integrating numerical simulation techniques, he has developed models that predict and control combustion risks in coal mines. His interdisciplinary approach combines thermodynamics, fluid mechanics, and chemistry to enhance the understanding of coal oxidation dynamics. His findings offer theoretical and practical solutions for mining operations in dry and windy regions, ensuring improved safety measures. With a strong commitment to industrial application, his work has influenced national policies on coal safety, and his collaborations with the National Natural Science Foundation of China have strengthened research advancements in the field. His studies continue to play a vital role in reducing environmental hazards and ensuring mine worker safety.

Publication Top Notes:

A study on the thermal behavior of coal gangue mountains under airflow influence based on MD and CFD simulations

New Insights into the Depressive Mechanism of Sodium Silicate on Bastnaesite, Parisite, and Fluorite: Experimental and DFT Study

Study on the air permeability characteristics of coal gangue dump slope gangue particles

Study on the variability of oxygen adsorption behavior in coal gangue based on pore size structure

Analysis of failure mode and deformation evolution characteristics of slopes under the influence of highwall mining

Experimental study and molecular simulation of spontaneous combustion of coal gangue by oxidation under different water contents

Study on adsorption-diffusion-seepage behavior of oxygen in coal gangue under the coupling of temperature and pressure

Study on the Law of Repeated Mining Strata Movement and Surface Subsidence in Close Distance Coal Seam

Synergistic Adsorption and Molecular Arrangement of Mixed Surfactants at the Air/Water Interface

DFT Calculation of BHA and SHA and Its Flotation Performance on Bastnaesite

Flotation effect and quantum chemical calculation of octyl hydroxamic acid and sodium silicate on bastnaesite and fluorite

Depression behavior and mechanism of sodium silicate on bastnaesite and parisite flotation

DFT calculation of octyl hydroxamic acid and oleic acid and their flotation performance comparison on bastnasite and fluorite

Mechanism of sodium sulfide on flotation of cyanide-depressed pyrite

Dr. Jean Moto Ongagna | Theoretical Chemistry | Catalysis Award

Dr. Jean Moto Ongagna | Theoretical Chemistry | Catalysis Award

Dr. Jean Moto Ongagna | Theoretical Chemistry | Lecturer – University of Douala-Cameroon , Cameroon

Dr. Jean Moto Ongagna is a Cameroonian researcher specializing in Theoretical chemistry and Computational Chemistry . He obtained his Ph.D. from the University of Douala in 2021. His expertise spans Density Functional Theory (DFT), Pharmacokinetics (ADMET), Molecular Docking, Molecular Dynamics (MD), and ab initio Molecular Dynamics (ADMP). Dr. Ongagna has contributed significantly to computational chemistry, particularly in studying metal complexes, chemical bonding, and reaction mechanisms. He has participated in prestigious international conferences and workshops, presenting groundbreaking research on chemical bonding interactions. With numerous publications in high-impact journals such as RSC Advances and the International Journal of Quantum Chemistry, his work advances the understanding of transition metal complexes and their applications. He actively collaborates with researchers worldwide and is dedicated to developing computational tools for chemical and biological systems.

Professional Profile : 

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Summary of Suitability for Award:

Dr. Jean Moto Ongagna is an outstanding candidate for the “Catalysis Awards”, given his significant contributions to computational catalysis and theoretical chemistry. His research extensively applies Density Functional Theory (DFT), Quantum Chemical Calculations, and Molecular Dynamics (MD) to investigate transition metal complexes, non-standard chemical bonding, and catalytic reaction mechanisms. His studies provide deep insights into metal-ligand interactions, catalytic efficiency, and reaction pathways, which are fundamental for designing novel catalytic systems. Dr. Jean Moto Ongagna’s research in computational catalysis, transition metal chemistry, and theoretical modeling aligns perfectly with the objectives of the “Catalysis Awards”. His work advances the understanding of catalyst behavior, reaction mechanisms, and molecular interactions, making a substantial impact on modern catalysis research. His ability to integrate quantum chemistry tools with catalytic design positions him as a highly suitable candidate for this prestigious recognition.

🎓Education:

Dr. Jean Moto Ongagna pursued his higher education at the University of Douala, Cameroon. He earned a Ph.D. in Theoretical and Computational Chemistry (2021), focusing on Density Functional Theory (DFT) and molecular simulations. In 2016, he completed his Master’s degree in the same field, where he explored the computational analysis of metal-ligand interactions. His Bachelor’s degree in Physical Chemistry (2013) laid the foundation for his research on quantum chemistry and molecular modeling. Before university, he completed his GCE Advanced Level (Baccalauréat D) in 2008 at Laic Private College “La Liberté” in Douala. His education equipped him with expertise in quantum chemistry, molecular docking, and theoretical modeling, enabling him to contribute to cutting-edge research. Throughout his academic journey, he attended specialized workshops and conferences to enhance his skills in computational chemistry, continuously refining his expertise in quantum simulations and advanced chemical theories.

🏢Work Experience:

Dr. Jean Moto Ongagna has extensive experience in Theoretical chemistry and Computational Chemistry, with expertise in Density Functional Theory (DFT), Molecular Docking, Pharmacokinetics (ADMET), and ab initio Molecular Dynamics (ADMP). He has actively participated in international conferences, presenting research on transition metal complexes, chemical bonding, and molecular interactions. He has contributed to significant projects involving the computational study of catalysts, biomolecular interactions, and pharmaceutical compounds. Dr. Ongagna has also collaborated with renowned institutions and researchers worldwide, publishing extensively in high-impact journals. His research experience includes developing and applying quantum chemical tools for investigating metal-ligand interactions and reaction mechanisms. He has been involved in multiple computational chemistry workshops, enhancing his knowledge of secondary metabolite discovery, quantum topology, and electronic structure theory. His contributions have led to a deeper understanding of non-standard chemical bonding and have implications for catalysis, drug design, and materials science.

🏅Awards: 

Dr. Jean Moto Ongagna has received multiple recognitions for his contributions to Theoretical and Computational Chemistry. He has been invited as a speaker at international conferences, including the 4th Commonwealth Chemistry Posters (2023) and the Virtual Conference on Chemistry and Its Applications (2021, 2022). His research on transition metal complexes and quantum chemistry has been published in high-impact journals such as RSC Advances and the International Journal of Quantum Chemistry. He has received accolades for his computational investigations on catalytic and biomolecular systems, contributing to the advancement of quantum chemical methodologies. His participation in scientific workshops at the University of Buea (Cameroon) and Technische Universität Dresden (Germany) further highlights his academic excellence. His continuous engagement in international scientific discussions and collaborations has strengthened his reputation as a leading researcher in quantum chemistry and molecular modeling.

🔬Research Focus:

Dr. Jean Moto Ongagna’s research focuses on Theoretical and Computational Chemistry, particularly Density Functional Theory (DFT), Quantum Chemical Calculations, Molecular Docking, Pharmacokinetics (ADMET), and Molecular Dynamics (MD). He specializes in studying transition metal complexes, non-standard chemical bonds, and catalytic reactions. His work involves topological analysis of chemical interactions using advanced computational techniques such as Quantum Theory of Atoms in Molecules (QTAIM), Energy Decomposition Analysis (EDA), and Natural Bond Orbital (NBO) analysis. He has made significant contributions to understanding palladium complexes, Diels–Alder reactions, and bioactive compounds. His research extends to computational drug discovery, antimicrobial compounds, and bioinorganic chemistry, aiming to bridge the gap between theoretical modeling and experimental applications. By integrating quantum chemical methods with molecular simulations, his studies provide valuable insights into reaction mechanisms, electronic structures, and potential applications in pharmaceuticals, catalysis, and material science.

Publication Top Notes:

Deciphering the Influence of Alkylene Bridged and Chelating Mode on Pd—C and Pd—X (X = Cl, Br, and I) Bonding Interaction Within Bis‐(NHC)‐Palladium Complexes Using Quantum Chemistry Tools

Authors: Gaël Mouzong D’Ambassa, Jean Moto Ongagna, Adjieufack Abel Idrice, Désiré Bikele Mama

Year: 2024

Computational Exploration of the Impact of Low‐Spin and High‐Spin Ground State on the Chelating Ability of Dimethylglyoxime Ligand on Dihalo Transition Metal: A QTAIM, EDA, and CDA Analysis

Authors: Daniel Lissouck, Suzane Leonie Djendo Mazia, Gaël Mouzong D’Ambassa, Jean Moto Ongagna

Year: 2024

Deciphering the Influence of PdII and PdIV Oxidation States on Non-Standard Chemical Bonds Within Bis(N-Heterocyclic Carbene) Complexes: Insights from DFT

Authors: Gaël Mouzong D’Ambassa, Jean Moto Ongagna, Adjieufack Abel Idrice, Désiré Bikele Mama

Year: 2024

Exploring the Mechanism of the Intramolecular Diels–Alder Reaction of (2E,4Z,6Z)-2(allyloxy)cycloocta-2,4,6-trien-1-one Using Bonding Evolution Theory

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2023

In Vitro and In Silico Studies of Antibacterial Activities of Secofriedelane Derivatives from Senna alata (L) Roxb

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2023

How a Chromium Tricarbonyl Complex Catalyzes the [3 + 2] Cycloaddition Reaction of N-Substituted Phenylnitrones with Styrene: A Molecular Electron Density Theory Analysis

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2023

Insight into the Antioxidant and Antiradical Properties of Colorotane Sesquiterpenes Extracted from Warburgia ugandensis: Theoretical Evaluation

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2021

Topological Unraveling of the [3+2] Cycloaddition (32CA) Reaction Between N-Methylphenylnitrone and Styrene Catalyzed by the Chromium Tricarbonyl Complex Using Electron Localization Function and Catastrophe Theory

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2021

B3LYP, M06 and B3PW91 DFT Assignment of nd8 Metal-Bis-(N-Heterocyclic Carbene) Complexes

Authors: Jean Moto Ongagna, Gaël Mouzong D’Ambassa

Year: 2020