Fakhrul Hasan Bhuiyan

Computational material scientist with experience in atomistic simulations, ML-based forcefiels, predictive models for molecules, and data analysis

Biography

Fakhrul (pronounced Folk-rule) is a postdoctoral researcher in the Computational Science Division. As a computational materials scientist, he specializes in providing atomistic insights into experimental findings and developing data analysis and visualization solutions. His expertise includes density functional theory calculations, molecular dynamics simulations, machine learning-based interatomic forcefield development, predictive model creation for molecules, data analysis, data scraping, and high-performance computing.

At Argonne, Fakhrul is part of the CSTEEL project, funded by the U.S. Department of Energy. His efforts in this project aim to create a computational framework and workflow for the rapid development of machine learning-based forcefields for atomistic simulations on Argonne’s supercomputers. Fakhrul’s work on forcefields has enabled molecular dynamics simulations of molten salts and electrolytes with transition metals, allowing for detailed analysis of their structure and dynamics, and corroborate experimental findings. He is also developing graph neural networks to predict redox potentials of iron-based metal complexes, aiming to facilitate the rapid screening of chemical spaces for electrochemical and catalytic applications.

Fakhrul earned his Ph.D. in Mechanical Engineering from the University of California, Merced, in 2024. His doctoral research involved using reactive molecular dynamics simulations and data analysis, in collaboration with experimental chemists, to investigate the molecular mechanisms of mechanochemical reactions. His projects also included studies on solid lubrication and lubricant additive reactions. Fakhrul was awarded the Nor-Cal STLE Research Scholarship in 2020 and the Graduate Dean’s Dissertation Fellowship in 2024 in recognition of his research efforts.

His current research interests focus on the development and application of machine learning models to study the structure, physics, and chemistry of organic and inorganic materials. Fakhrul is keen on developing automated frameworks and workflows for large-scale supercomputers and exploring the use of large language models through agentic frameworks to automate data scraping and analysis tasks. Collaboration is central to Fakhrul’s research approach, and he actively seeks collaborative projects that integrate experimental and computational teams, believing that teamwork enhances scientific discovery.

Personal Website: https://fbhuiyan.pages.dev

Google Scholar: https://scholar.google.com/citations?user=DeueGDEAAAAJ&hl=en