@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 = {

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.

}, 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 {48944, title = {Solid state hydrogen storage: Decoding the path through machine learning}, journal = {International Journal of Hydrogen Energy }, volume = {50}, year = {2024}, month = {JAN}, pages = {1518-1528}, type = {Article}, abstract = {

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.

}, keywords = {Enthalpy of hydride formation, Hydrogen storage capacity, Metal hydrides, Predictive machine learning models, Solid-state hydrogen storage}, issn = {0360-3199}, doi = {10.1016/j.ijhydene.2023.10.056}, author = {Verma, Ashwini and Wilson, Nikhil 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 = {

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.

}, 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} }