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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Developing a Foundational Inter-Atomic Potential for Transitional Metal Alloys Using Active Learning
Author(s)	Brenden W. Hamilton, Benjamin T Nebgen, Avanish Mishra, Mashroor Nitol, Nithin Mathew, Saryu Fensin, Timothy C Germann
On-Site Speaker (Planned)	Brenden W. Hamilton
Abstract Scope	We develop a machine learned inter-atomic potential (MLIAP) for molecular dynamics simulations of 11 different transition metals. This ‘foundation-like’ model, while functional as a stand-alone MLIAP for metal alloy simulations, it also serves as a basis for transfer learning a more accurate model for narrower composition spaces. We utilize an active learning framework to determine structures and compositions of high uncertainty for training, which consists of three steps, starting with only single element compositions, then building in binary and ternary alloys in subsequent steps of the active learning procedure. Due to the large compositional space of all possible ternary alloys, we develop an approach that spans compositions in addition to forces and energies in order to minimize the training data needed. We present the development of this high composition space training algorithm and model accuracy for various test cases of a range of alloys including 5 component systems.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Modeling and Simulation, High-Entropy Alloys

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