2026-10-13T14:35:35Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34030844 2026-09-30T09:00:09Z category_146 category_915 category_7 category_734 category_931 category_133 portal_87 item_type_3 month_year_09_2026

Machine learning-driven analysis and process optimization of FePt-BN granular films for HAMR applications Daisuke Ogawa (178677) Tetsuya Shoji (2206045) Masao Yano (9286267) Yusuke Matsuoka (2560795) Yukiko K. Takahashi (10509892) Space Science Physical Sciences not elsewhere classified Medicine Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology FePt-BN granular films process optimization machine learning <p>The ever-increasing demand for high-density, energy-efficient data storage, propelled by AI and cloud infrastructures, is driving advancements in heat-assisted magnetic recording (HAMR) media. L1₀-ordered FePt granular thin films are recognized as leading candidates owing to their exceptional thermal stability and potential for sub-10 nm grain sizes. Here, we investigate FePt-BN granular films, systematically optimizing their microstructure and magnetic properties by tuning process parameters including BN atomic fraction of FePt-BN targets, N₂ gas flow, and film thickness. Employing a hybrid workflow that integrates conventional trial-and-error experimentation with machine learning such as principle component analysis, random forest regression and Bayesian optimization, we identify key determinants governing average main- and sub-grain size (<i>D</i>₁, <i>D</i>₂), coercivity (<i>μ</i>₀<i>H</i>_c), and chemical ordering. Machine-learning analysis reveals that the degree of L1₀ order is the primary factor for <i>μ</i>₀<i>H</i>_c, while <i>D</i>₁ is mainly influenced by BN content and microstructural attributes derived from transmission electron microscopy data. 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> 2026-09-30T09:00:09Z Dataset Dataset 10.6084/m9.figshare.34030844.v1 https://figshare.com/articles/dataset/Machine_learning-driven_analysis_and_process_optimization_of_FePt-BN_granular_films_for_HAMR_applications/34030844 CC BY 4.0