Bayesian Optimization of the Experimental Parameters of Material Synthesis: Application to the Magnesioreduction of Rare-Earth-Free (Mn1–xFex)5Si3 Magnetocalorics
Year: 2025
Type: article
Abstract: Optimizing complex synthesis processes remains a significant challenge in materials science, particularly when high phase purity must be achieved in complex multivariable systems with restricted experimental throughput. Here, we demonstrate the application of a fully experimental Bayesian Optimization (BO) framework to efficiently optimize the synthesis conditions of (Mn 1– x Fe x ) 5 Si 3, a rare-earth-free magnetocaloric material. Using magnesioreduction as the synthesis route, seven experimental parameters were iteratively refined via active learning to maximize phase purity. Phase-pure samples, including the challenging peritectoid-forming Fe 5 Si 3, were obtained within five active learning iterations. The approach generalizes to unseen compositions and yields chemically meaningful insights via SHAP analysis of the trained Gaussian Process model, identifying maximum temperature and SiO 2 excess as key drivers of phase purity. X-ray diffraction and scanning electron microscopy confirmed high crystallinity, uniform submicron grain size, and accurate compositional control in the optimized samples. Magnetic measurements revealed a near-room-temperature magnetocaloric effect in ferromagnetic Mn 1.25 Fe 3.75 Si 3, with a peak −Δ S mag of 1.06 J kg –1 K –1 at 275 K under a 2 T magnetic field change. This study establishes BO as a powerful and data-efficient framework for accelerating the synthesis of functional materials from small experimental data sets.
Source: Chemistry of Materials
Authors: Sylvain Le Tonquesse, Laura Agnarelli, Swathi Sakthivel
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413064184