About Me

I am Arsalan Akhtar, a passionate researcher and academic with a deep-rooted fascination for materials science. My journey into the world of science began in Iran, where I completed my Master’s thesis on the electronic and optical properties of MgO nanosheets, employing density functional theory. This initial foray into computational materials science set the foundation for my future endeavors, sparking a curiosity that would lead me across continents and academic institutions. With the prestigious Severo Ochoa Ph.D. Scholarship, I furthered my studies at the Universitat Autònoma de Barcelona, delving into the complexities of ionic mobilities near interfaces in multifunctional oxides. During this time, I developed innovative tools and workflows to compute defect formation energies and migration barriers, pushing the boundaries of what was known in crystalline systems. My doctoral research, under the guidance of esteemed professors like Pablo Ordejón and Miguel Pruneda , allowed me to master advanced computational tools such as SIESTA and become proficient in multiple programming languages, enhancing my versatility as a researcher. This period was not just about learning but also about contributing to the broader scientific community, as I played a key role in the development of new features for the SIESTA computational tool, and was actively involved in several European research projects like NFFA and MAX.

In my postdoctoral role, my focus has shifted towards integrating cutting-edge machine learning techniques with traditional computational models to accelerate materials discovery. At UCLouvain, I have been at the forefront of developing interface between machine learning models and Abinit, significantly improving the speed and efficiency of structural optimization tasks.

My commitment to innovation is evident in my ongoing efforts to interface SIESTA with Atomate2, aiming to enhance the efficiency and user-friendliness of computational tools used in our field. With a solid background in both theoretical and applied research, my work reflects a deep commitment to advancing our understanding of materials at the atomic level. I am excited to continue pushing the frontiers of material science, applying my skills in computational modeling, machine learning, and code development to solve complex problems and contribute to groundbreaking discoveries.