Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science
Major
Chemical Engineering
College
College of Arts and Sciences
Research Mentor
Neeraj Rai
Research Mentor's Department
Dave C. Swalm School of Chemical Engineering
Research Center
Center for Computational Sciences
Abstract
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
Molybdenum phosphides are promising catalysts for biomass conversion, particularly in the hydrogenation of organic molecules. Hydrogen dissociation on the surface of such catalysts is a critical step in the process. Traditional computational methods such as density functional theory (DFT) can provide valuable insights into hydrogen-catalyst interactions but are limited by their high computational cost and inefficiency for large systems and long time scales. Machine learning has emerged as a solution to these limitations. Machine learned interatomic potentials (MLIPs), trained on ab initio molecular dynamics (AIMD) simulations, enable the development of molecular dynamics (MD) for much larger systems over much longer time scales, with significantly reduced computational cost. This allows more complex interactions and rare reactive events to be observed that would otherwise be inaccessible with traditional methods. In this study, MACE, a message passing neural network, is employed to develop MLIPs for the dissociation of hydrogen on molybdenum phosphide surfaces. Utilizing MACE results in highly accurate MD simulations with time scales on the order of 1,000 times longer than those achievable with AIMD. These MD simulations provide key insights about the system, including preferred adsorption sites and atomic interactions and serve as a bridge to studying biomass conversion. This study demonstrates the efficacy of machine learning for catalytic research, illustrating the robust and efficient predictive capabilities of MLIPs and underscoring the potential of machine learning techniques for the efficient discovery, optimization, and understanding of new catalysts.
Presentation Date
Summer 8-2-2024
Keywords
machine learning, catalysis, molybdenum phosphide
Recommended Citation
Lugo, Jeremy; Lane, John Michael; Wilson, Woodrow N.; and Rai, Neeraj, "Developing Machine Learned Interatomic Potentials for Hydrogen Dissociation over Molybdenum Phosphide" (2024). Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science. 3.
https://scholarsjunction.msstate.edu/ccs-reu/3