Unprecedented solvent induced inter-conversion between monomeric and dimeric silylene-zinc iodide adducts - Yadav, Sandeep; Sangtani, Ekta; Dhawan, Diksha; Gonnade, Rajesh G.; Ghosh, Debashree; Sen, Sakya S. (Dalton Transaction, 2017)
<div class="block-record-info" style="margin: 0px 22px 22px; list-style: none; padding: 0px; line-height: 20px; font-size: 13px; color: rgb(51, 51, 51); font-family: arial, helvetica, sans-serif; background-color: rgb(248, 248, 248);"> Usually, when a silylene reacts with a transition metal Lewis acid, it forms an adduct which could be either monomeric or dimeric. However, we present here that a silylene, [PhC(NtBu)(2)SiN(SiMe3)(2)] can form both monomeric [PhC(NtBu)(2)Si{N(SiMe3)(2)} -> ZnI2]center dot THF (1) and dimeric [{PhC(NtBu)(2)}(N(SiMe3)(2))SiZnI,(mu-I)](2) (2) adducts upon reaction with ZnI2. The formation of 1 and 2 relies upon the solvent used for the reaction or crystallization. When the crystallization is carried out in THF complex 1 is formed, however, when the reaction and crystallization are performed in acetonitrile complex 2 is obtained. Both 1 and 2 were structurally authenticated and the nature of the Si-Zn bond in these complexes was determined by quantum chemical calculations. In addition, a spontaneous inter-conversion between 1 and 2 just by changing the solvents has been also observed; a feature presently not known for silylene-transition metal Lewis adducts.
Keywords: Bis(Silylene ); Carbene Complexes; Crystal -structure Determination; Dative bond; Lewis acid base reaction; Ligands; Main- group compounds; Reactivity; Silicon(II) Bis(Trimethylsilyl)Amide; Carbonyl-Complexes

Machine learning prediction of interaction energies in rigid water clusters - Bose, Samik; Dhawan, Diksha; Nandi, Sutanu; Sarkar, Ram Rup; Ghosh, Debashree (Physical Chemistry Chemical Physics, 2018)
Classical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol(-1) and 0.34 kcal mol(-1), respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.