@article {48061,
	title = {Looking beyond adsorption energies to understand interactions at surface using machine learning},
	journal = {ChemistrySelect},
	volume = {7},
	year = {2022},
	month = {OCT},
	pages = {e202202414},
	type = {Article},
	abstract = {<p>
	Identifying factors that influence interactions at the surface is still an active area of research. In this work, the importance of analyzing bond length activations (BLact) along with adsorption energies (E-a) while interpreting Density Functional Theory (DFT) results is emphasized. Investigating adsorption of different small molecules, such as O-2, N-2, CO, and CO2, on commonly studied facets ((100), (110), and (111)) of seven fcc transition metal surfaces (M=Ag, Au, Cu, Ir, Rh, Pt, and Pd) demonstrates the missing linear correlation between E-a and BLact. Further, tree based Machine Learning (ML) models reinforce the missing linear correlation between the two parameters and also highlight the importance of analyzing both to develop a better understanding of adsorption at surfaces. The best performing Random Forest models have a mean absolute error (MAE) of 0.19 eV for E-a prediction, and even lower MAE of 0.012 angstrom for BLact prediction. While often d-band center is correlated with E-a, our observations show that infact the d-band center has a better correlation with BLact. These observations emphasizes the role of BLact in gaining a fuller picture for catalysis. The fact that the factors responsible for BLact is a lesser-explored subject adds to the novelty of the findings.</p>
},
	keywords = {Adsorption energy, Bondlength activation, catalysis, DFT, machine learning},
	issn = {2365-6549},
	doi = {10.1002/slct.202202414},
	author = {Agarwal, Sheena and Joshi, Kavita}
}
@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 = {<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>
},
	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}
}
