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

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.

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-classes02676nas a2200229 4500008004100000022001400041245007700055210006900132260000800201300001400209490000700223520189800230653003402128653003002162653001902192653003902211653003302250100001902283700001902302700001802321856010702339 2024 eng d a0360-319900aSolid state hydrogen storage: Decoding the path through machine learning0 aSolid state hydrogen storage Decoding the path through machine l cJAN a1518-15280 v503 a

We present a machine learning (ML) framework HEART (HydrogEn storAge propeRty predicTor) for identifying suitable families of metal alloys for hydrogen storage under ambient conditions. Our framework includes two ML models that predict the hydrogen storage capacity (HYST) and the enthalpy of hydride formation (THOR) of multi-component metal alloys. We demonstrate that a chemically diverse set of features effectively describes the hydrogen storage properties of the alloys. In HYST, we use absorption temperature as a feature which improved H2wt% prediction significantly. For out-of-the-bag samples, HYST predicted H2wt% with R2 score of 0.81 and mean absolute error (MAE) of 0.45 wt% whereas R2 score is 0.89 and MAE is 4.53 kJ/molH2 for THOR. These models are further employed to predict H2wt% and Delta H for similar to 6.4 million multi-component metal alloys. We have identified 6480 compositions with superior storage properties (H2wt% > 2.5 at room temperature and Delta H < 60 kJ/molH2). We have also discussed in detail the interesting trends picked up by these models like temperature dependent variation in the rate of hydrogenation and alloying effect on H2wt% and Delta H in different families of alloys. Importantly certain elements like Al, Si, Sc, Cr, and Mn when mixed in small fractions with hydriding elements like Mg, Ti, V etc. systematically reduce Delta H without significantly compromising the storage capacity. Further upon increasing the number of elements in the alloy i.e from binary to ternary to quaternary, the number of compositions with lower enthalpies also increases. From the 6.4 million compositions, we have reported new alloy families having potential for hydrogen storage at room temperature. Finally, we demonstrate that HEART has the potential to scan vast chemical spaces by narrowing down potential materials for hydrogen storage.

10aEnthalpy of hydride formation10aHydrogen storage capacity10aMetal hydrides10aPredictive machine learning models10aSolid-state hydrogen storage1 aVerma, Ashwini1 aWilson, Nikhil1 aJoshi, Kavita uhttps://library.ncl.res.in/content/solid-state-hydrogen-storage-decoding-path-through-machine-learning01780nas 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

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.

10aAdaptive learning10aHydrogen storage10aMetal hydrides10aPCT isotherms1 aVerma, Ashwini1 aJoshi, Kavita uhttps://library.ncl.res.in/content/pathfinder-adaptive-learning-hydrogen-storage-material-optimizationa02403nas 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

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.

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