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ORIGINAL RESEARCH ARTICLE

Machine-learned molecular modeling of ruthenium: A Kolmogorov-Arnold Network approach

Zhiyu An1 ,  Jingjie Yeo2*
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1 Department of System Engineering, Cornell University, Ithaca, New York, United States of America
2 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, United States of America
IJAMD, 8291 https://doi.org/10.36922/ijamd.8291
Submitted: 30 December 2024 | Revised: 26 January 2025 | Accepted: 7 February 2025 | Published: 25 February 2025
(This article belongs to the Special Issue Applications of Deep Learning in Advanced Materials Processing)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Developing refractory high-entropy superalloys (RSAs) with performance advantages over nickel-based alloys is a critical frontier in materials science. Body-centered cubic (bcc)-based RSAs have attracted significant attention, with ruthenium (Ru) playing a key role in forming two-phase regions of A2 (disordered bcc) + B2 (ordered bcc), which could lead to superalloy-like microstructures. This study introduces the application of the Kolmogorov-Arnold Network (KAN) model to predict the mechanical and thermodynamic properties of Ru while comparing its performance against other commonly used machine-learned models. Utilizing density functional theory calculations as training data, the KAN model demonstrates superior accuracy and computational efficiency compared to conventional methods, while reducing descriptor complexity. The model accurately predicts a range of properties, including elastic constants, thermal expansion coefficients, and various moduli, with discrepancies within 6% of experimental reference data. Molecular dynamics simulations further validate the model’s efficacy, accurately capturing Ru’s phase transitions from hexagonal close-packed (hcp) to face-centered cubic structure and the melting point. This work presents the first application of KAN in materials science, demonstrating how its balanced performance and efficiency provide a new pathway for designing advanced materials, with unique advantages over conventional machine learning approaches in predicting material properties.

Graphical abstract
Keywords
Ruthenium
Kolmogorov-Arnold Network
Machine learning
Mechanical properties
Thermodynamic properties
Density-functional theory
Molecular dynamics
Funding
J.Y. acknowledges support from the US NSF under the award CMMI-2338518.
Conflict of interest
Jingjie Yeo is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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