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In our recent study, we propose using machine learning (ML) to correct differences in properties calculated at two quantum chemical (QC) methods with different accuracy.
In the Δ-ML approach ML model is trained on differences between some property \(P_t\) calculated at the target level of theory and property \(P’_b\) calculated at the baseline level of theory. Then this ML model is used to predict \(\Delta_b^t\) for out-of-sample molecules.
We propose using machine learning (ML) for improving semiempirical Hamiltonian. Given sufficiently large training set ML can be used to correct parameters of semiempirical quantum chemical (SQC) method individually for any target molecule.
Such automatic parametrization technique (APT) stands in stark contrast to the traditional special-purpose reparametrization (SPR), when parameters are optimized for specific type of molecules and then resulting rSQC method is used unchanged for every other target molecule.
For our studies we used subset of huge database published by us. Hybrid ML-SQC approach has much lower error in predicted atomization enthalpies in comparison with SQC method with standard parameters.
In our study we reported synthesis, and experimental and theoretical characterization of new one-dimensional coordination polymers. Research article “Multiply Bonded Metal(II) Acetate (Rhodium, Ruthenium, and Molybdenum) Complexes with the trans-1,2-Bis(N-methylimidazol-2-yl)ethylene Ligand” (DOI: 10.1021/ic501435a) was published on November 13th in the Inorganic Chemistry. The experimental work was done in the group of Prof. Dr. Nicolai Burzlaff and theoretical part in the group of Prof. Dr. Timothy Clark.