@article {50131,
	title = {Pathfinder: adaptive learning for hydrogen storage material optimizationa},
	journal = {International Journal of Hydrogen Energy},
	volume = {236},
	year = {2026},
	month = {MAY },
	pages = {155124},
	type = {Article},
	abstract = {<p>
	Progress in solid-state hydrogen storage is constrained by time-consuming experiments and scarce high-quality data, limiting effective use of machine learning. To address this, we present an adaptive learning (AL) framework that integrates uncertainty quantification within a closed-loop workflow for targeted material optimization. Unlike static models, it adaptively selects compositions to maximize information gain and improve predictive performance. As a proof of concept, we predict pressure-composition-temperature (PCT) isotherms of Mg-Ni-La systems using literature data for Mg-Ni. The framework identifies informative compositions across Mg fractions (92\%-4\%) and temperatures (300-633 K), demonstrating effective exploration of the chemical space. For ten unseen compositions evaluated sequentially, accuracy reaches 80\% within five cycles, with predictions aligning well with experiments. This establishes a family-specific predictive tool for Mg-Ni-based systems, while the underlying AL framework is broadly applicable to other chemical families with modest initial experimental datasets.</p>
},
	keywords = {Adaptive learning, Hydrogen storage, Metal hydrides, PCT isotherms},
	issn = {0360-3199},
	doi = {10.1016/j.ijhydene.2026.155124},
	author = {Verma, Ashwini and Joshi, Kavita}
}
