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 H for 6.4 million multi-component metal alloys. We have identified 6480 compositions with superior storage properties (H2wt% 2.5 at room temperature and 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 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 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.

CO2, a byproduct from various industrial reactions, must not be released into the atmosphere and should be managed through capture, conversion, and utilization. The first step in converting CO2 into valuable products is to break the C–O bond. This work focuses on designing Single Atom Catalysts (SACs) by doping Cu(211) surface with 13 different s, p, and d block elements with an aim to minimize the activation barrier for C–O bond cleavage. Our work demonstrates that SACs of Mg/Al/Pt@Cu(211) favour CO2 chemisorption compared to Cu(211) where CO2 physisorbs. The barrier for CO2 dissociation is lowest for Mg@Cu(211) and it increases in the order Mg@Cu(211), Al@Cu(211), Pt@Cu(211), Zn@Cu(211) Ga@Cu(211) Cu@Cu(211) Pd@Cu(211). These findings suggest that doping Cu(211) with earth-abundant metals like Mg can potentially be a viable catalyst for CO2 conversion, providing a promising solution to reduce carbon footprint and mitigate climate change.

CO$_2$, a well-known greenhouse gas, is a potential raw material that can produce various chemicals. Dissociation of CO$_2$ to CO or hydrogenation to formate (HCOO$^*$) or carboxyl (COOH$^*$) intermediate is crucial in determining the reaction pathway for CO$_2$ conversion. In this work, we demonstrate that alloys of Mg-Cu exhibit greater activity toward activation and hydrogenation of CO$_2$ than transition metal alloys reported so far. Two different compositions of Mg-Cu, namely Mg$_2$Cu and MgCu$_2$, have been studied using periodic Density Functional Theory (DFT). Our investigations reveal that CO$_2$ chemisorbs on both intermetallic alloys. Coadsorption of CO$_2$ with H$_2$O leads to the spontaneous formation of COOH$^*$ over Mg$_2$Cu(224), whereas a negligible barrier (0.04 eV) is observed for MgCu$_2$(311). HCOO$^*$ formation has a barrier of 0.34 eV and 0.42 eV on Mg$_2$Cu(224) and MgCu$_2$(311), respectively. Dissociation of CO$_2$ to CO is kinetically unfavourable on both compositions of Mg-Cu. We provide a rationale for the observed activity by analyzing the electronic structure. Notably, the spontaneous hydrogenation of CO$_2$ makes earth-abundant metals suitable candidates for alloying that await experimental verification.

Extensive research has contributed to a better understanding of the commercially important epoxidation reaction. Selectivity, a crucial aspect of this reaction, has received significant attention in both experimental and theoretical investigations. However, a consensus regarding the role of electrophilic oxygen in epoxidation is yet to be reached. The present study is a theoretical examination of the prerequisites necessary for direct epoxidation to occur on the Ag(100) surface, at varied monolayer concentrations. Additionally, the study investigates the characteristics of various oxygen species interacting with ethylene to promote the direct epoxidation pathway. Based on the effective charges and projected density of states (pDOS) analysis, three oxygen variants were identified on the Ag(100) surface: atomic oxygen, dissociatively adsorbed molecular oxygen, and O . The investigation reveals that all oxygen species, despite their physical and electronic differences, are electrophilic and undergo direct epoxidation. This work provides insights into the complex nature of epoxidation reaction and discusses electronic factors influencing the selective oxidation route on different Agsingle bondO complexes.

Methane is a major constituent of natural gas and is widely used in hydrogen production. However, its high symmetry poses a challenge, as breaking the strong C-H bond requires substantial energy input. Hence, there is a pressing need to develop efficient catalysts for methane conversion. By synergizing theory and experimentation, the search for a better catalyst can be accelerated, potentially boosting methane conversion processes. In the present work, theoretical findings prompted the experiments, which revealed the spontaneous dissociation of CH4 on selected facets of ?-Ga2O3. Additionally, the activation barrier for ethane formation was merely 0.1 eV. NTP-assisted conversion of methane in the presence of ?-Ga2O3 confirmed these findings. The formation rate of hydrogen and ethane rises to 366 µmolg?1h?1 and 86.62 µmolg?1h?1, respectively, in the presence of ?-Ga2O3, in contrast to 281.4 µmolg?1h?1 and 66 µmolg?1h?1 without catalysts. For the CH4-H2O reaction in the presence of ?-Ga2O3, there is an increase of 74.42% in the CO formation rate compared to the reaction without the catalyst. An electronic structure analysis revealed that electrophilic oxygen species on the ?-Ga2O3 (-202) surface play a vital role in the decomposition of methane, facilitating C-H bond cleavage.

In this work, we introduce the HyStor database, consisting of 1280 metal alloys along with their hydrogen storage capacities (H2wt%) as a function of absorption temperature. Given the lack of updates in the existing open-access HydPark database since 2002, we sourced compositions from recent research articles and various patent documents, resulting in a total of 468 compositions. The addition is reflected in the data across all existing classes of alloy compositions, and low entropy alloys (LEA), medium entropy alloys (MEA), and high entropy alloys (HEA) have been included. This has broadened the scope of the database to encompass the latest materials of interest for hydrogen storage. HyStor contains a representation of 54 elements, with a temperature range of 200-800K and an H2wt% range of 0.1-7.19. To ensure data quality, we conducted thorough checks for duplicate entries, erroneous data, and conflicting compositions within the database. Furthermore, we conducted multiple tests to identify potential outlier compositions by benchmarking the database against the pre-trained HYST model on HydPark data. After eliminating these potential outliers, we successfully improved the error metrics of the HYST model, reducing the Mean Absolute Error (MAE) from 0.32 to 0.28 and increasing the R2 score from 0.78 to 0.82. We also tested individual classes and observed that the performance of the HYST model has increased for the majority of the classes.

Partial oxidation of methanol to value-added products presents an intriguing and challenging process. Among these products, formaldehyde is the simplest and one of the most vital aliphatic aldehydes, with extensive application across various domains. Industrially, silver and iron-molybdenum oxides are used as catalysts for converting methanol to formaldehyde at elevated temperatures (600? C and 250-400? C, respectively). However, in this computational and experimental study, we have demonstrated the efficacy of ZnO as a catalyst. Notably, in the presence of ZnO, methanol readily converts to formaldehyde even under ambient conditions. We employed periodic density functional theory (DFT) to explore the (1011) facet of ZnO to elucidate its interaction with methanol. Our comprehensive analysis identified the most active facet (1011) involved in the spontaneous conversion of methanol to formaldehyde. Subsequently, experimental validation supported our theoretical findings, demonstrating the conversion of methanol to formaldehyde with 100% selectivity at room temperature and atmospheric pressure in the presence of ZnO. This study exemplifies the pivotal role of theory in catalyst design.

The generation of low-energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter-atomic interaction description. In this work, we formulate the search algorithm as a reinforcement learning (RL) problem. Concisely, we propose a novel actor-critic architecture that generates low-lying isomers of metal clusters at a fraction of computational cost than conventional methods. Our RL-based search algorithm uses a previously developed DART model as a reward function to describe the inter-atomic interactions to validate predicted structures. Using the DART model as a reward function incentivizes the RL model to generate low-energy structures and helps generate valid structures. We demonstrate the advantages of our approach over conventional methods for scanning local minima on potential energy surface. Our approach not only generates isomer of gallium clusters at a minimal computational cost but also predicts isomer families that were not discovered through previous density-functional theory (DFT)-based approaches.

We have investigated various Zn-based catalysts for their interaction with methanol (MeOH). MeOH is one of the most critical molecules being studied extensively, and Zn-based catalysts are widely used in many industrially relevant reactions involving MeOH. We note that the same element (Zn and O, in the present study) exhibits different catalytic activity in different environments. The changing environment is captured in the underlying electronic structure of the catalysts. In the present work, we compared the electronic structure of Zn-based systems, i.e., ZnAl2O4 and ZnO along with oxygen preadsorbed Zn (O-Zn) and metallic Zn. We demonstrate the one-to-one correlation between the pDOS of the bare facet and the outcome of that facet’s interaction (i.e. either adsorption or dissociation of MeOH) with MeOH. These findings would pave the way towards the in-silico design of catalysts.

In this work, we propose a mixed metal oxide as a catalyst and demonstrate it's ability to not only activate the MeOH molecule upon adsorption but also dissociate O-H and one of it's C-H bonds. MeOH activation is compared on two prominent facets of \znalo~ viz. (220) and (311). While spontaneous O-H bond dissociation is observed on both facets, C-H bond dissociates only on the (311) surface. Multiple factors like atomic arrangement and steps on the surface, coordination of surface atoms, and their effective charges have a combined effect on MeOH activation. The (311) surface offers higher catalytic activity in comparison with (220) surface. Having a stepped surface, availability of multiple sites, and variation in the charge distribution are some of the reasons for better catalytic performance of (311) facet. Effect of orientation of MeOH with respect to the surface adds both, information and complexity to the problem. Observations pertinent to understanding this effect are also reported. A detailed analysis of atomic arrangement on the two surfaces provides a rationale as to why MeOH gets dissociated spontaneously on the mixed metal oxide. The promising results reported here opens up a new class of catalyst for research.

Increasing demand of ethylene oxide and the cost of versatile chemical ethene has been a driving force for understanding mechanism of epoxidation to develop highly selective catalytic process. Direct epoxidation is a proposed mechanism which in theory provides 100% selectivity. A key aspect of this mechanism is an electrophilic oxygen (Oele) species forming on the Ag surface. In the past two and half decades, large number of theoretical and experimental investigations have tried to elucidate formation of Oele on Ag surface with little success. Equipped with this rich literature on Ag-O interactions, we investigate the same using periodic DFT calculations to further understand how silver surface and oxygen interact with each other from a chemical standpoint. Based on energetics, Löwdin charges, topologies and pdos data described in this study, we scrutinize the established notions of Oele. Our study provides no evidence in support of Oele being an atomic species nor a diatomic molecular species. In fact, a triatomic molecular species described in this work bears multiple signatures which are very convincing evidence for considering it as the most sought for electrophilic entity.