Fakhrul Hasan Bhuiyan, Ph.D.
Computational Material Scientist
Solving materials problems through atomistic simulations, AI/ML, and data analysis
Fakhrul (Folk-Rule) is a postdoctoral researcher in the Computational Science Division at Argonne National Laboratory. His expertise includes density functional theory calculations, molecular dynamics simulations, machine learning-based interatomic forcefield development, graph neural network creation for molecules, data analysis, data scraping, and high-performance computing. He is passionate about developing automated frameworks for large-scale supercomputers and seeks collaborative projects that integrate experimental and computational teams.
Latest News
Selected to attend the ATPESC 2025 to be held in St. Charles, IL, from July 27-August 8
July 2025
Will be attending the 248th ECS Meeting in Chicago to present latest work on graph neural networks, reduction potential, and metal complexes
October 2025
Received LDRD seed funding ($50k) to develop visual language models to analyze cyclic voltammetry figures
May 2025
About
Fakhrul H Bhuiyan is a mechanical engineer and a computational material scientist. He received his Ph.D. in mechanical engineering in 2024 from the University of California, Merced. His doctoral research involved the investigation of the molecular mechanisms of mechanochemical reactions using reactive molecular dynamics simulations and data analysis, in collaboration with experimental chemists. Currently, he is working as a postdoctoral researcher in the Computational Science Division at Argonne National Laboratory.
Fakhrul's current research as a postdoc focuses on the application of machine learning and AI in atomistic simulations of electrolytes and molten salts containing transition metals. In addition, he develops tools and packages for the Argonne Leadership Computing Facility supercomputers to accelerate and streamline materials simulations. His current research is funded by the Department of Energy's C-STEEL project.
Highlights
Projects
ML-Forcefields for Molten Salts
Developing machine learning-based forcefields for accurate molecular dynamics simulations of molten salts containing transition metals.
GNN for Redox Potentials
Creating graph neural network models to predict redox potentials of iron-based metal complexes for rapid screening.
Mechanochemistry Simulations
Investigating molecular mechanisms of mechanochemical reactions using reactive molecular dynamics simulations.
Contact
Fakhrul is always looking for new opportunities, collaborations, and connections. Feel free to contact him with any questions or just to say hi!
fbhuiyan@anl.gov
Address
Bldg. 240, 9700 S Cass Ave, Lemont, IL 60439