@article {49316,
	title = {HyStor: an experimental database of hydrogen storage properties for various metal alloy classes},
	journal = {International Journal of Hydrogen Energy},
	volume = {90},
	year = {2024},
	month = {NOV },
	pages = {460-469},
	type = {Article},
	abstract = {<p>
	In this work, we introduce the HyStor database, consisting of 1282 metal alloys along with their maximum hydrogen storage capacity (H2wt\%) at a given absorption temperature. The curated HydPark database consist of 831 entries. We sourced compositions from research articles and various patent documents, resulting in addition of 451 compositions to the HydPark database. The addition is reflected in the data across all existing classes of alloys. Further, low entropy alloys (LEA), medium entropy alloys (MEA) and high entropy alloys (HEA) have been newly included classes. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains representation of 54 elements, with a temperature range of 200-800 K, and H2wt\% ranging from 0.1 to 7.19. We conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database to ensure data quality. Furthermore, we conducted multiple tests to identify potential outlier compositions. The data curation and updation reflects into slight improved error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.31 to 0.29 and increasing the R2 score from 0.77 to 0.79.</p>
},
	keywords = {Databases, machine learning, Metal hydrides, Solid-state hydrogen storage},
	issn = {0360-3199},
	doi = {10.1016/j.ijhydene.2024.09.390},
	author = {Wilson, Nikhil and Verma, Ashwini and Maharana, Piyush Ranjan and Sahoo, Ameeya Bhusan and Joshi, Kavita}
}
@article {50070,
	title = {What drives property prediction for solid-state hydrogen storage? data or smart features?},
	journal = {International Journal of Hydrogen Energy},
	volume = {226},
	year = {2026},
	month = {APR },
	pages = {154499},
	type = {Article},
	abstract = {<p>
	Metal hydrides play a pivotal role in a wide range of applications, including hydrogen storage, compression, heat management, and catalysis, making them a central focus of interdisciplinary research spanning chemistry, materials science, and engineering. The performance of the metal hydride-based systems is strongly governed by the thermodynamics of metal-hydrogen interactions. Among key thermodynamic properties, the equilibrium plateau pressure (P-eq) is particularly critical, as it defines operating conditions for hydrogen absorption and desorption. Traditionally, determining P-eq requires extensive experimental measurements, which limits the pace of materials discovery. On the other hand, predicting it through ML-based models is constrained by the availability of limited data. In this work, we demonstrate that smart features can be a way to overcome this limitation. EquiP, an ML model trained to predict ln(P-eq) as a function of temperature, generates Van{\textquoteright}t Hoff plots (P-eq vs. 1/T), enabling rapid determination of enthalpy and entropy of hydride formation. We demonstrate that incorporating structural descriptors derived from X-ray diffraction (XRD) data improves the performance of the model, particularly with sparse training datasets. A model trained using only compositional descriptors yields a validation mean absolute error (MAE) of 0.21 bar, whereas incorporating XRD features reduces the MAE substantially to 0.07 bar. This work demonstrates that with limited data, intelligent feature design grounded in domain knowledge is the key to improving predictions of complex material properties.</p>
},
	keywords = {Experimental data, Feature engineering, Hydrogen storage, machine learning, Metal hydrides, Property prediction},
	issn = {0360-3199},
	doi = {10.1016/j.ijhydene.2026.154499},
	author = {Verma, Ashwini and Joshi, Kavita}
}
