Iqtidar Ahmad | photocatalytic water splitting | Best Researcher Award

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Dr. Iqtidar Ahmad | photocatalytic water splitting | Best Researcher Award

Postdoctoral fellow, Shenzhen University, China.

Dr. Iqtidar Ahmad is a Pakistani physicist specializing in material physics and chemistry, currently serving as a Postdoctoral Researcher at the College of Materials Science and Engineering, Shenzhen University, China. He completed his Ph.D. in 2022 at Kunming University of Science and Technology, China. Dr. Ahmad has held teaching positions in Pakistan, including at Government Degree College, Lohor, and Army Public School and College, Mansehra. His research focuses on low-dimensional materials, van der Waals heterostructures, and their applications in optoelectronics, spintronics, and photocatalysis. He has co-authored several publications in high-impact journals, contributing significantly to the field of material science.

Profile

Orcid

Education 

Dr. Ahmad’s academic journey began with a Diploma of Associate Engineering (D.A.E.) in Electronics from Gandahara College of Technology, Chakdara, Pakistan, in 2009. He then pursued a Bachelor of Science (Hons) in Physics at Hazara University Mansehra, Pakistan, graduating in 2013 with a CGPA of 3.42/4. Continuing his studies, he completed a Master of Philosophy (M.Phil.) in Physics at the same institution in 2016, achieving a CGPA of 3.92/4. Dr. Ahmad further advanced his expertise by earning a Ph.D. in Material Physics and Chemistry from Kunming University of Science and Technology, China, in December 2022. His educational background laid a strong foundation for his research in material science and physics.

Experience 

Dr. Ahmad has a diverse professional background combining academia and research. He currently serves as a Postdoctoral Researcher at the College of Materials Science and Engineering, Shenzhen University, China, since 2023. Prior to this, he held teaching positions in Pakistan, including Lecturer roles at Government Degree College, Lohor (2016–2017), Army Public School and College, Mansehra (2015–2016), and Suffa Model School (2013–2014). His research experience encompasses computational studies on two-dimensional materials and their applications in energy-related fields. Dr. Ahmad’s work has led to several publications in peer-reviewed journals, reflecting his commitment to advancing knowledge in material science.

Research Focus 

Dr. Ahmad’s research primarily focuses on the theoretical investigation of low-dimensional materials and their heterostructures, utilizing first-principles calculations to explore their electronic, optical, and thermoelectric properties. His work aims to design materials with enhanced performance for applications in optoelectronics, spintronics, and photocatalysis. He employs advanced computational techniques, including density functional theory (DFT), to study phase transitions, strain engineering, and the effects of doping and adsorption on material properties. Dr. Ahmad’s research contributes to the development of materials with tailored properties for energy-related applications, such as water splitting and energy storage. His expertise in computational material science positions him at the forefront of research in this domain.

Publication Top Notes

  1. Title: Two-dimensional SiH/In₂XY (X, Y = S, Se) van der Waals heterostructures for efficient water splitting photocatalysis: A DFT approach

    • Journal: International Journal of Hydrogen Energy

    • Date: April 18, 2025

    • DOI: 10.1016/j.ijhydene.2025.04.289

    • Summary: This study investigates the photocatalytic properties of SiH/In₂XY heterostructures for water splitting applications, utilizing density functional theory to analyze their efficiency.

  2. Title: Theoretical insights into Sb₂Te₃/Te van der Waals heterostructures for achieving very high figure of merit and conversion efficiency

    • Journal: International Journal of Heat and Mass Transfer

    • Date: March 1, 2025

    • DOI: 10.1016/j.ijheatmasstransfer.2024.126479

    • Summary: This paper explores the thermoelectric properties of Sb₂Te₃/Te heterostructures, aiming to enhance their efficiency for energy conversion applications.

  3. Title: The van der Waals heterostructures of blue phosphorene with GaN/GeC for high-performance thermoelectric applications

    • Journal: APL Materials

    • Date: January 1, 2025

    • DOI: 10.1063/5.0243511

    • Summary: This research examines the potential of blue phosphorene/GaN/GeC heterostructures for thermoelectric applications, focusing on their performance and efficiency.

  4. Title: Enhanced spintronic and electronic properties in MTe₂-GdCl₂ (M=Mo, W) heterojunctions

    • Journal: Surfaces and Interfaces

    • Date: December 2024

    • DOI: 10.1016/j.surfin.2024.105364

    • Summary: This paper investigates the spintronic and electronic

  5. Title: Enhanced visible-light-driven photocatalytic activity in SiPGaS/arsenene-based van der Waals heterostructures

    • Journal: iScience

    • Date: 2023

    • DOI: 10.1016/j.isci.2023.108025

    • Summary: Demonstrates enhanced visible-light absorption and charge separation efficiency in SiPGaS/arsenene heterostructures, making them promising candidates for photocatalytic water splitting.

  6. Title: High thermoelectric performance of two-dimensional SiPGaS/As heterostructures

    • Journal: Nanoscale

    • Date: 2023

    • DOI: 10.1039/d3nr00316g

    • Summary: Investigates thermoelectric efficiency improvements through phonon suppression and high Seebeck coefficients in SiPGaS/As heterostructures.

  7. Title: Nickel selenide nano-cubes anchored on cadmium selenide nanoparticles for hybrid energy storage

    • Journal: Journal of Energy Storage

    • Date: 2023

    • DOI: 10.1016/j.est.2023.107065

    • Summary: First-ever design of NiSe nanocubes on CdSe for hybrid supercapacitor applications showing high capacitance and stability.

  8. Title: Versatile characteristics of Ars/SGaInS van der Waals heterostructures

    • Journal: Physical Chemistry Chemical Physics

    • Date: 2023

    • DOI: 10.1039/d2cp04832a

    • Summary: Analyzes multifunctional characteristics for applications in optoelectronics and photovoltaics.

  9. Title: Two-dimensional Janus SGaInSe/PtSe₂ heterostructures for water splitting

    • Journal: International Journal of Hydrogen Energy

    • Date: 2022

    • DOI: 10.1016/j.ijhydene.2022.06.188

    • Summary: Examines potential for solar-driven water splitting, emphasizing electron-hole separation efficiency.

  10. Title: Electronic, mechanical, and photocatalytic properties of Janus XGaInY monolayers

    • Journal: RSC Advances

    • Date: 2021

    • DOI: 10.1039/d1ra02324a

    • Summary: Explores tunable bandgaps and mechanical stability of Janus monolayers for photocatalysis.

Conclusion

Dr. Iqtidar Ahmad is a highly qualified, technically capable, and productive researcher in the field of computational materials science. His work demonstrates depth, novelty, and interdisciplinary relevance, making him a strong candidate for a Best Researcher Award, especially at the early to mid-career level.

Donovan Birky | Interpretable Machine Learning for Material Modeling | Best Researcher Award

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Mr. Donovan Birky | Interpretable Machine Learning for Material Modeling | Best Researcher Award

Graduate Research Assistant , University of Utah ,United States

 

Donovan Birky is a PhD candidate in Mechanical Engineering at the University of Utah, with a passion for applying computational techniques and machine learning to solve complex material modeling problems. His expertise spans finite element simulation, machine learning for constitutive model development, uncertainty quantification, and high-performance computing. He has contributed to innovative research in areas like plasticity modeling, microstructure-sensitive damage models, and uncertainty analysis. Birky has collaborated with prominent institutions like Sandia National Labs, where he worked on material damage models and neural networks for material science applications. His work has resulted in multiple impactful publications in renowned journals and conferences. Outside of research, Birky has excelled academically, earning recognition such as the Rhode Baker Most Outstanding Student award and multiple placements on the Dean’s List. His interdisciplinary work bridges material science, engineering, and machine learning, with a focus on real-world applications in industry.

Profile

Education

Donovan Birky is currently pursuing a PhD in Mechanical Engineering at the University of Utah, where he has maintained a GPA of 3.93/4.00 since 2020. In Fall 2022, he received his Master of Science degree in Mechanical Engineering from the same institution. As part of his doctoral research, Birky is a member of the Materials Prognosis from Integrated Modeling and Experiment (M’) Lab, contributing to the development of advanced material damage models using machine learning. Prior to his graduate studies, Birky earned a Bachelor of Science in General Engineering from Fort Lewis College, Durango, CO, graduating with a GPA of 3.92/4.00. During his time at Fort Lewis, he was awarded the Rhode Baker Most Outstanding Student award for Physics and Engineering and was consistently on the Dean’s List. Birky also contributed as a STEM tutor for the TRIO Student Success Center, further demonstrating his dedication to academic excellence.

Experience

Donovan Birky has significant research experience as a Graduate Research Assistant at the University of Utah since 2020, working in the Materials Prognosis from Integrated Modeling and Experiment (M’) Lab. His research focuses on the integration of machine learning with material modeling to develop damage models for advanced materials. His previous internships at Sandia National Laboratories have further refined his skills in computational mechanics and material science. In 2021 and 2022, Birky worked on projects developing grain growth and pore growth datasets, using Sierra finite element codes for machine learning applications. He also contributed to the development of a material model calibration tool. Birky’s research at Fort Lewis College, funded by Sandia National Labs, centered on applying AI and machine learning to solve structural dynamics problems. His work at both academic and research institutions showcases his ability to tackle complex engineering challenges using cutting-edge computational techniques and high-performance computing.

Research Focus

Donovan Birky’s research is centered around computational material modeling and machine learning, with a particular focus on improving the accuracy and interpretability of constitutive models used in material science. His work integrates advanced techniques such as finite element simulation, genetic programming-based symbolic regression (GPSR), and uncertainty quantification to develop more robust models for material damage and plasticity. Birky is also interested in creating microstructure-sensitive deformation models that can better predict material behavior under varying conditions. A key aspect of his research is the application of high-performance computing for running large-scale simulations and training machine learning models, enhancing model reliability and efficiency. His work includes developing yield surface models for porous metals and improving existing damage models, like the Gurson model, through symbolic regression. Birky’s research aims to bridge the gap between fundamental material science and industry, offering tools for product design and certification in real-world engineering applications.

Publications

  • Predicting the dynamic response of a structure using an artificial neural network 📊💻 (2022)
  • Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression 📐🔬 (2023)
  • Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions 🔄⚙️ (2023)
  • Physics-Informed Machine Learning for the Development of Microstructure-Sensitive Deformation and Damage Models 🧬🔍 (2021)
  • Methods for Generating Interpretable Yield Surface Models With UQ Based on Data With Multiple Sources of Uncertainty 📈📚 (2024)
  • Interpretability and Generalizability of Constitutive Models using Symbolic Regression 🤖📏 (2024)
  • Uncertainty Quantification for Interpretable Constitutive Models using Genetic Programming-based Symbolic Regression 🎲💡 (2023)
  • Interpretable machine learning for uncertain, microstructure-dependent constitutive models 🔍🧪 (2023)
  • Physics-informed Machine Learning for Development of Interpretable, Improved Material Damage Models 🔬🔧 (2022)
  • A Data-driven Approach for Improving the Existing Gurson Material Damage Model Using Genetic Programming for Symbolic Regression 📊📉 (2022)
  • Improved Plasticity and Damage Models By Symbolic Regression of Microscale Finite Element Simulations 🧠📐 (2021)
  • Efficient Clustering of the Dynamic Response of a Structure Subject to Impulse Loading ⚡📊 (2021)
  • Learning Implicit Yield Surface Models with Uncertainty Quantification for Noisy Datasets 🌐🔍 (2021)
  • Applying genetic programming symbolic regression to solid mechanics 🔧🧠