Machine learning-driven analysis and process optimization of FePt-BN granular films for HAMR applications

Active-learning-guided Bayesian optimization enabled us to rapidly achieve FePt-BN media with high <i>μ</i>₀<i>H</i>_c (2.4 T), sub 6 nm grain diameters, and areal grain densities exceeding 12 Tgrains/in² outperforming traditional heuristic approaches with fewer experiments. Our results underscore the power of data-driven process optimization for accelerated HAMR materials development, enabling the realization of next-generation hard disk drives with ultra-high recording densities.</p> <p>This study establishes a data-driven framework for understanding and optimizing nanostructured magnetic materials, using FePt-BN granular thin films for heat-assisted magnetic recording (HAMR) as a model system.</p> <p>The central novelty lies in the integration of advanced data analytics with experimental materials science to extract physically meaningful structure–property relationships from complex, high-dimensional datasets.

By combining principal component analysis of XRD and TEM data with machine-learning models, we demonstrate that coercivity is governed not simply by grain size, but more fundamentally by sub-grain structure and grain-boundary characteristics – providing new physical insight into magnetization stability in nanoscale systems.</p> <p>Furthermore, this work introduces an active-learning-based experimental loop, in which Bayesian optimization directly guides thin-film synthesis, enabling rapid convergence toward optimal microstructures with significantly reduced experimental cost.

This approach transforms conventional empirical process optimization into a systematic and reproducible methodology.</p> <p>Beyond the specific FePt-BN system, the proposed framework is broadly applicable to a wide range of functional materials, offering a general strategy for accelerating materials discovery and design through the fusion of experimental techniques and data science.</p>