@proceedings {43316,
	title = {Design and engineering of organic molecules for customizable terahertz tags},
	volume = {8985},
	year = {2014},
	month = {FEB},
	pages = {Article Number: UNSP 89850P},
	publisher = {SPIE-Int Soc Optical Engineering, 1000 20th ST, PO Box 10, Bellingham, WA 98227-0010 USA},
	address = {San Francisco, CA},
	abstract = {<p>Terahertz (THz) frequency band lies between the microwave and infrared region of the electromagnetic spectrum. Molecules having strong resonances in this frequency range are ideal for realizing "Terahertz tags" which can be easily incorporated into various materials. THz spectroscopy of molecules, especially at frequencies below 10 THz, provides valuable information on the low frequency vibrational modes, viz. intermolecular vibrational modes, hydrogen bond stretching, torsional vibrations in several chemical and biological compounds. So far there have been very few attempts to engineer molecules which can demonstrate customizable resonances in the THz frequency region. In this paper, Diamidopyridine (DAP) based molecules are used as a model system to demonstrate engineering of THz resonances (\&lt; 10 THz) by fine-tuning the molecular mass and bond strengths. Density Functional Theory (DFT) simulations have been carried out to explain the origin of THz resonances and factors contributing to the shift in resonances due to the addition of various functional groups. The design approach presented here can be easily extended to engineer various organic molecules suitable for THz tags application.</p>},
	doi = {10.1117/12.2041194},
	author = {Ray, Shaumik and Dash, Jyotirmayee and Nallappan, Kathirvel and Kaware, Vaibhav and Basutkar, Nitin and Ambade, Ashootosh and Joshi, Kavita and Pesala, Bala}
}
@article {47701,
	title = {DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers},
	journal = {Physical Chemistry Chemical Physics},
	volume = {23},
	year = {2021},
	month = {OCT },
	pages = {21995-22003},
	type = {Article},
	abstract = {Recently, 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.},
	issn = {1463-9076},
	doi = {10.1039/d1cp02956h},
	author = {Modee, Rohit and Agarwal, Sheena and Verma, Ashwini and Joshi, Kavita and Priyakumar, U. Deva}
}
@article {47393,
	title = {Disordered but efficient: understanding the role of structure and composition of the Co-Pt alloy on the electrocatalytic methanol oxidation reaction},
	journal = {Journal of Physical Chemistry C },
	volume = {125},
	year = {2021},
	month = {APR },
	pages = {7611-7624},
	type = {Article},
	abstract = {<p>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.</p>},
	issn = {1932-7447},
	doi = {10.1021/acs.jpcc.0c10165},
	author = {Dalavi, Shankar B. and Agarwal, Sheena and Deshpande, Pooja and Joshi, Kavita and Bhagavatula L. V. Prasad}
}
