<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Verma, Ashwini</style></author><author><style face="normal" font="default" size="100%">Joshi, Kavita</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathfinder: adaptive learning for hydrogen storage material optimizationa</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Hydrogen Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptive learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydrogen storage</style></keyword><keyword><style  face="normal" font="default" size="100%">Metal hydrides</style></keyword><keyword><style  face="normal" font="default" size="100%">PCT isotherms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">236</style></volume><pages><style face="normal" font="default" size="100%">155124</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	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.&lt;/p&gt;
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	Foreign&lt;/p&gt;
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	9.2&lt;/p&gt;
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