Interpretable machine learning-guided design of Fe-based soft magnetic alloys

2025-04-28

AI Summary

Designing high-performance, cobalt- and nickel-free soft magnetic materials—particularly Fe–Si–B and multicomponent Fe-based alloys—requires accurate prediction and mechanistic understanding of saturation magnetization (Mₛ) and coercivity (H꜀). Method: We develop an interpretable machine learning framework integrating uncertainty quantification, SHAP-based feature attribution, and partial dependence analysis. This framework combines XGBoost modeling, density functional theory (DFT) electronic structure calculations, and melt-spinning experimental validation. Contribution/Results: Our approach enables the first quantitative decoupling of the nonlinear physical mechanisms governing Mₛ and H꜀. For the Fe–Si–B system (Mₛ = 1.54–2.09 T), prediction error is <3%. Critically, inverse design yields a Co/Ni-free alloy with Mₛ = 2.01 T and H꜀ < 10 A/m—performance comparable to commercial NANOMET® and FINEMET®—demonstrating a data-driven paradigm for rational soft magnetic material design.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and B content reduces MS from 1.81T (DFT~2.04 T) to ~1.54 T (DFT~1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09T), Fe-5Si-5B (2.01T) and Fe-10Si-10B (1.54T) alloy compositions further support our findings. These trends are consistent with density functional theory (DFT) predictions, which link increased electronic disorder and band broadening to lower MS values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveals that MS is governed by a nonlinear interplay between Fe content, early transition metal ratios, and annealing temperature, while HC is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudo-quaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe84.8Si0.5B9.4Cu0.8 P3.5C1), FINEMET (Fe73.5Si13.5B9 Cu1Nb3), NANOPERM (Fe88Zr7B4Cu1), and HITPERM (Fe44Co44Zr7B4Cu1. Our fundings demonstrate the potential of ML framework for accelerated search of high-performance, Co- and Ni-free, soft magnetic materials.

Problem

• Predicting saturation magnetization and coercivity in Fe-rich soft magnetic alloys.
• Understanding how Si and B content affect magnetic properties in Fe-Si-B systems.
• Accelerating the search for Co- and Ni-free high-performance soft magnetic materials.

Innovation

• Machine learning predicts Fe alloy magnetic properties
• DFT validates ML results on electronic disorder effects
• Uncertainty quantification reveals nonlinear compositional interplay