02048nas a2200217   4500008004100000022001400041245010000055210006900155260000800224300001500232490000600247520131900253653002201572653002601594653001401620653000801634653002101642100002001663700001801683856012901701       2022                        eng d  a2365-654900aLooking beyond adsorption energies to understand interactions at surface using machine learning0 aLooking beyond adsorption energies to understand interactions at  cOCT  ae2022024140 v73 a<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>
10aAdsorption energy10aBondlength activation10acatalysis10aDFT10amachine learning1 aAgarwal, Sheena1 aJoshi, Kavita  uhttps://library.ncl.res.in/content/looking-beyond-adsorption-energies-understand-interactions-surface-using-machine-learning