02676nas 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<p>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% &gt; 2.5 at room temperature and Delta H &lt; 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.</p>
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-learning