@article {47649,
	title = {Lowering the C-H bond activation barrier of methane by means of SAC@Cu(111): periodic DFT investigations},
	journal = {New Journal of Chemistry},
	volume = {46},
	year = {2021},
	month = {DEC },
	pages = {70-74},
	type = {Article},
	abstract = {Methane has long captured the world{\textquoteright}s attention for being the simplest yet one of the most notorious hydrocarbons. Exploring its potential to be converted into value-added products has raised compelling interest. In the present work, we have studied the efficiency of single-atom catalysts (SACs) for methane activation employing density functional theory (DFT). The climbing image-nudged elastic band (CI-NEB) method is used in tandem with the improved dimer (ID) method to determine the minimum energy pathway for the first C-H bond dissociation of methane. Our study reported that the transition-metal doped Cu(111) surfaces enhance the adsorption, activate the C-H bond, and reduce the activation barrier for first C-H bond cleavage of methane. The results suggest Ru-/Co-/Rh-doped Cu(111) as promising candidates for methane activation with a minimal activation barrier and a less endothermic reaction. For these SACs, the calculated activation barriers for the first C-H bond cleavage are 0.17 eV, 0.24 eV, and 0.26 eV respectively, which is substantially lower than 1.13 eV, the activation barrier for Cu(111).},
	issn = {1144-0546},
	doi = {10.1039/d1nj04525c},
	author = {Bhati, Meema and Dhumal, Jignesh and Joshi, Kavita}
}
@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}
}
