Researcher  Focus and Expertise

My research lies at the intersection of computational materials science, quantum physics, and machine learning, with a specific focus on designing and discovering novel materials for sustainable energy applications. Through first-principles calculations and data-driven approaches, I investigate the fundamental properties of materials critical to energy conversion, storage, and catalysis. Based on my published work in the past few years, I have developed particular expertise in:

• Computational design and characterization of two-dimensional materials for hydrogen storage and generation, with emphasis on transition metal dichalcogenides and their derivatives
• First-principles investigation of electronic, magnetic, and transport properties of nanomaterials for energy applications
• Machine learning approaches to predict catalytic activity and selectivity in electrocatalytic processes
• Density functional theory studies of defect engineering in materials for enhanced energy conversion efficiency
• Multi-scale modeling of materials for rechargeable batteries, including electrode materials and solid electrolytes
• Structure-property relationships in materials for CO₂ and N₂ electrochemical reduction

My computational work combines density functional theory calculations with machine learning techniques to accelerate the discovery of materials with tailored properties, bridging the gap between theoretical predictions and experimental applications in sustainable energy technologies.

Current Appointments

Researcher AI4Mat Initiative

Dutch Institute for Fundamental Energy Research (DIFFER), Eindhoven, Netherlands 

At DIFFER, I am part of the Artificial Intelligence for Materials Discovery (AI4Mat) initiative, where I contribute to the development of computational frameworks for accelerating materials discovery. My current work focuses on:

• Developing machine learning models to predict and optimize electrocatalytic performance
• Creating computational workflows that integrate quantum mechanical simulations with data-driven approaches
• Investigating structure-property relationships in catalytic materials for nitrogen and carbon dioxide reduction
• Contributing to the Autonomous Energy Materials Discovery (AEMD) team’s efforts to address challenges in electrocatalysis and hydrogen production
  
  

Guest Researcher at Materials Science and Engineering

Delft University of Technology (TU Delft), Netherlands

At TU Delft, I collaborate with both computational and experimental groups in interdisciplinary research on energy materials. My research activities include:

• Computational studies of materials for advanced rechargeable batteries
• Investigation of sustainable hydrogen production pathways
• Analysis of hydrogen embrittlement mechanisms in steels
• Modeling of hydrogen-storage nanomaterials
• Computational design of electrocatalysts for CO₂ reduction

This collaborative environment enables the validation of computational predictions through experimental testing, strengthening the practical impact of theoretical insights.

Research Background

My journey in computational materials science began with a Master’s degree in Mathematical Physics from Mohammed V University in Rabat, where I focused on the mathematical foundations of density functional theory (DFT). This theoretical foundation provided me with a deep understanding of quantum mechanical methods that would later become central to my research approach. During my Master’s studies, I had the privilege of attending multiple workshops and summer schools at the International Center for Theoretical Physics (ICTP) in Trieste, Italy, which expanded my theoretical toolkit and exposed me to cutting-edge computational methods. These experiences were pivotal in shaping my research interests at the intersection of theoretical physics and materials science.

My doctoral journey began through a competitive Africa-Sweden Research Cooperation grant, which enabled me to conduct collaborative research between Moulay Ismail University in Morocco and Uppsala University in Sweden. Under this framework, I worked with Prof. Abdelmajid Ainane and Prof. Rajeev Ahuja on my dissertation titled “Materials Modelling for Energy Harvesting: From Conversion to Application Through Storage.” This research established novel computational approaches for predicting and optimizing materials properties across the energy cycle, from generation to storage, with particular emphasis on two-dimensional and nanostructured materials. The quality of my doctoral work led to my selection for J. Gustaf Richert Stiftelse Postdoctoral Fellowship at Uppsala University’s Materials Theory Group within the Department of Physics and Astronomy. During this fellowship, I expanded my research to focus on computational studies of sustainable energy production and storage using ultra-thin nanomaterials. I developed expertise in applying advanced computational methods to predict electronic, magnetic, optical, and transport properties of novel materials, with particular emphasis on their applications in hydrogen storage, battery technologies, and catalysis.

Throughout this academic journey, I have consistently published in high-impact journals, contributing to the fundamental understanding of materials for energy applications. My work has evolved from pure theoretical investigations to more applied computational approaches that bridge theory with experimental validation. This evolution reflects my growing interest in addressing real-world energy challenges through computational materials design. My current positions at DIFFER and TU Delft represent the next phase in this academic trajectory, where I am integrating my expertise in quantum mechanical simulations with cutting-edge machine learning approaches to accelerate materials discovery for sustainable energy technologies. This combination of traditional computational materials science with artificial intelligence methods allows me to address increasingly complex challenges in materials design for energy applications.

Research Philosophy

I believe that computational approaches, when properly integrated with experimental validation, can dramatically accelerate the discovery and optimization of materials for clean energy technologies. My research philosophy centers on developing methods that bridge multiple scales and disciplines, connecting fundamental quantum mechanical insights with practical materials design in a closed-loop approach. I am committed to pushing the boundaries of computational efficiency and accuracy in materials modeling, particularly through the integration of physics-based simulations with data-driven approaches. By combining these complementary methods, we can overcome the limitations of traditional computational approaches and enable more rapid discovery of materials with tailored properties for energy applications.

My work aims not only to predict materials properties but also to provide fundamental insights into the underlying mechanisms that govern materials behavior. This understanding is essential for designing next-generation materials that can address global energy challenges, from renewable energy conversion to efficient storage and utilization. As I continue to develop my research program, I remain dedicated to fostering collaborative environments that bring together computational and experimental expertise. These collaborations strengthen the impact of theoretical predictions by enabling experimental validation and refinement, ultimately accelerating the transition of designed materials from computational models to practical applications in sustainable energy technologies.