02057nas a2200217   4500008004100000245008000041210006900121260011700190300003200307490000900339520122200348100001701570700002201587700002501609700002001634700002001654700002201674700001801696700001701714856010801731       2014                        eng d00aDesign and engineering of organic molecules for customizable terahertz tags0 aDesign and engineering of organic molecules for customizable ter  aSan Francisco, CAbSPIE-Int Soc Optical Engineering, 1000 20th ST, PO Box 10, Bellingham, WA 98227-0010 USAcFEB  aArticle Number: UNSP 89850P0 v89853 a<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>1 aRay, Shaumik1 aDash, Jyotirmayee1 aNallappan, Kathirvel1 aKaware, Vaibhav1 aBasutkar, Nitin1 aAmbade, Ashootosh1 aJoshi, Kavita1 aPesala, Bala  uhttps://library.ncl.res.in/content/design-and-engineering-organic-molecules-customizable-terahertz-tags02619nas 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<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>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-alloy