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.
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)SiN(SiMe)] can form both monomeric [PhC(NtBu)Si{N(SiMe)} → ZnI]·THF (1) and dimeric [{PhC(NtBu)}(N(SiMe))SiZnI,(μ-I)] (2) adducts upon reaction with ZnI. 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.
We present a two-step procedure called
the dynamical self-energy
mapping (DSEM) that allows us to find a sparse Hamiltonian representation
for molecular problems. In the first part of this procedure, the approximate
self-energy of a molecular system is evaluated using a low-level method
and subsequently a sparse Hamiltonian is found that best recovers
this low-level dynamic self-energy. In the second step, such a sparse
Hamiltonian is used by a high-level method that delivers a highly
accurate dynamical part of the self-energy that is employed in later
calculations. The tests conducted on small molecular problems show
that the sparse Hamiltonian parameterizations lead to very good total
energies. DSEM has the potential to be used as a classical–quantum
hybrid algorithm for quantum computing where the sparse Hamiltonian
containing only O(n2) terms on a Gaussian
orbital basis, where n is the number of orbitals
in the system, could reduce the depth of the quantum circuit by at
least an order of magnitude when compared with simulations involving
a full Hamiltonian.
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