02043nas a2200265 4500008004100000022001400041245007100055210006900126260000800195300001100203490000800214520127000222653000801492653000901500653002901509653001201538100001801550700002001568700002001588700002501608700001701633700001301650700001801663856009601681 2020 eng d a0169-433200aMixed metal oxide: a new class of catalyst for methanol activation0 aMixed metal oxide a new class of catalyst for methanol activatio cDEC a1474490 v5343 a

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 ZnAl(2)O(4 )viz. (2 2 0) and (31 1). While spontaneous O-H bond dissociation is observed on both facets, C-H bond dissociates only on the (3 1 1) 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 (3 1 1) surface offers higher catalytic activity in comparison with (2 2 0) 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 (3 1 1) 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.

10aDFT10aMeOH10aSpontaneous dissociation10aZnAl2O41 aMehta, Shweta1 aAgarwal, Sheena1 aKenge, Nivedita1 aMekala, Siva, Prasad1 aPatil, Vipul1 aRaja, T.1 aJoshi, Kavita uhttps://library.ncl.res.in/content/mixed-metal-oxide-new-class-catalyst-methanol-activation02295nas a2200169 4500008004100000022001400041245010700055210006900162260000900231300001400240490000700254520167800261100002001939700001801959700001801977856013001995 2020 eng d a1144-054600aUnderstanding the ML black box with simple descriptors to predict cluster-adsorbate interaction energy0 aUnderstanding the ML black box with simple descriptors to predic cMAY  a8545-85530 v443 a

Density functional theory (DFT) is currently one of the most accurate and yet practical theories used to gain insight into the properties of materials. Although successful, the computational cost required is still the main hurdle even today. In recent years, there has been a trend of combining DFT with Machine Learning (ML) to reduce the computational cost without compromising accuracy. Finding the right set of descriptors that are simple to understand in terms of giving insights about the problem at hand, lies at the heart of any ML problem. In this work, we demonstrate the use of nearest neighbor (NN) distances as descriptors to predict the interaction energy between the cluster and an adsorbate. The model is trained over a size range of 5 to 75 atom clusters. When the training and testing is carried out on mutually exclusive cluster sizes, the mean absolute error (MAE) in predicting the interaction energy is similar to 0.24 eV. MAE reduces to 0.1 eV when testing and training sets include information from the complete range. Furthermore, when the same set of descriptors are tested over individual sizes, the MAE further reduces to similar to 0.05 eV. We bring out the correlation between dispersion in the nearest neighbor distances and variation in MAE for individual sizes. Our detailed and extensive DFT calculations provide a rationale as to why nearest neighbor distances work so well. Finally, we also demonstrate the transferability of the ML model by applying the same recipe of descriptors to systems of different elements like (Na-10), bimetallic systems (Al6Ga6, Li4Sn6, and Au40Cu40) and also different adsorbates (N-2, O-2, and CO).

1 aAgarwal, Sheena1 aMehta, Shweta1 aJoshi, Kavita uhttps://library.ncl.res.in/content/understanding-ml-black-box-simple-descriptors-predict-cluster-adsorbate-interaction-energy02619nas a2200193 4500008004100000022001400041245016300055210006900218260000900287300001600296490000700312520188200319100001702201700002002218700001902238700001802257700002102275856012902296 2021 eng d a1463-907600aDART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers0 aDART deep learning enabled topological interaction model for ene cOCT  a21995-220030 v233 aRecently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol(-1) (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.1 aModee, Rohit1 aAgarwal, Sheena1 aVerma, Ashwini1 aJoshi, Kavita1 aPriyakumar, Deva uhttps://library.ncl.res.in/content/dart-deep-learning-enabled-topological-interaction-model-energy-prediction-metal-clusters01902nas a2200193 4500008004100000022001400041245015300055210006900208260000900277300001400286490000800300520117000308100002401478700002001502700002101522700001801543700003001561856011701591 2021 eng d a1932-744700aDisordered but efficient: understanding the role of structure and composition of the Co-Pt alloy on the electrocatalytic methanol oxidation reaction0 aDisordered but efficient understanding the role of structure and cAPR  a7611-76240 v1253 a

A systematic investigation of the electrocatalytic Methanol Oxidation Reaction (MOR) was carried out using a model Co:Pt alloy system with different compositions and structural arrangements of the Co and Pt atoms. The structural variations with the same alloy composition included those with disordered arrangement of Co and Pt atoms in a face-centered cubic (fcc) lattice and ordered arrangements in face-centered tetragonal (fct) lattices. Our investigations clearly show that structures with disordered arrangements with Co:Pt atomic ratios near to 1:1 display better electrocatalytic efficiencies even when compared to pure Pt. These experimental findings were then rationalized by means of Density Functional Theory (DFT) calculations. Electronic level signatures in terms of charge transfer and relative shift in the peaks of the d band for surface metal atoms are proposed to be the reasons for the superior catalytic activity of a particular composition over the others. An increase in the number of inequivalent sites for methanol adsorption in disordered systems appears to result in better catalytic performance in comparison with ordered systems.

1 aDalavi, Shankar, B.1 aAgarwal, Sheena1 aDeshpande, Pooja1 aJoshi, Kavita1 aPrasad, Bhagavatula L. V. uhttps://library.ncl.res.in/content/disordered-efficient-understanding-role-structure-and-composition-co-pt-alloy02048nas 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

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.

10aAdsorption energy10aBondlength activation10acatalysis10aDFT10amachine learning1 aAgarwal, Sheena1 aJoshi, Kavita uhttps://library.ncl.res.in/content/looking-beyond-adsorption-energies-understand-interactions-surface-using-machine-learning