01780nas a2200205   4500008004100000022001400041245007800055210006900133260000900202300001100211490000800222520111900230653002201349653002101371653001901392653001801411100001901429700001801448856010801466       2026                        eng d  a0360-319900aPathfinder: adaptive learning for hydrogen storage material optimizationa0 aPathfinder adaptive learning for hydrogen storage material optim  cMAY   a1551240 v2363 a<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>
10aAdaptive learning10aHydrogen storage10aMetal hydrides10aPCT isotherms1 aVerma, Ashwini1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/pathfinder-adaptive-learning-hydrogen-storage-material-optimizationa