01825nas a2200181   4500008004100000245008000041210006900121260000800190300001600198490000700214520121800221100001601439700001901455700001801474700002101492700002101513856010901534       2018                        eng d00aMachine learning prediction of interaction energies in rigid water clusters0 aMachine learning prediction of interaction energies in rigid wat  cSEP  a22987-229960 v203 aClassical 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.1 aBose, Samik1 aDhawan, Diksha1 aNandi, Sutanu1 aSarkar, Ram, Rup1 aGhosh, Debashree  uhttps://library.ncl.res.in/content/machine-learning-prediction-interaction-energies-rigid-water-clusters