02066nas a2200241   4500008004100000022001400041245010000055210006900155260000900224300001200233490000700245520125000252653001401502653002101516653001901537653003301556100001901589700001901608700002901627700002601656700001801682856012401700       2024                        eng d  a0360-319900aHyStor: an experimental database of hydrogen storage properties for various metal alloy classes0 aHyStor an experimental database of hydrogen storage properties f  cNOV   a460-4690 v903 a<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>
10aDatabases10amachine learning10aMetal hydrides10aSolid-state hydrogen storage1 aWilson, Nikhil1 aVerma, Ashwini1 aMaharana, Piyush, Ranjan1 aSahoo, Ameeya, Bhusan1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/hystor-experimental-database-hydrogen-storage-properties-various-metal-alloy-classes02403nas a2200229   4500008004100000022001400041245009400055210006900149260000900218300001100227490000800238520163600246653002201882653002401904653002101928653002101949653001901970653002401989100001902013700001802032856012302050       2026                        eng d  a0360-319900aWhat drives property prediction for solid-state hydrogen storage? data or smart features?0 aWhat drives property prediction for solidstate hydrogen storage   cAPR   a1544990 v2263 a<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'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>
10aExperimental data10aFeature engineering10aHydrogen storage10amachine learning10aMetal hydrides10aProperty prediction1 aVerma, Ashwini1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/what-drives-property-prediction-solid-state-hydrogen-storage-data-or-smart-features