Hierarchical machine learning of potential energy surfaces.

We present hierarchical machine learning (hML) of highly accurate potential energy surfaces (PESs). Our scheme is based on adding predictions of multiple Δ-machine learning models trained on energies and energy corrections calculated with a hierarchy of quantum chemical methods. Our (semi-)automatic procedure determines the optimal training set size and composition of each constituent machine learning model, simultaneously minimizing the computational effort necessary to achieve the required accuracy of the hML PES. Machine learning models are built using kernel ridge regression, and training points are selected with structure-based sampling. As an illustrative example, hML is applied to a high-level ab initio CH3Cl PES and is shown to significantly reduce the computational cost of generating the PES by a factor of 100 while retaining similar levels of accuracy (errors of ∼1 cm-1)

A p-stacked porphyrin-fullerene electron donor-acceptor conjugate that features a surprising frozen geometry

A “frozen” electron donor– acceptor array that bears porphyrin and fullerene units covalently linked through the ortho position of a phenyl ring and the nitrogen of a pyrrolidine ring, respectively, is reported. Electrochemical and photophysical features suggest that the chosen linkage supports both through-space and throughbond interactions. In particular, it has been found that the porphyrin singlet excited state decays within a few picoseconds by means of a photoinduced electron transfer to give the rapid formation of a long-lived charge-separated state. Density functional theory (DFT) calculations show HOMO and LUMO to be localized on the electrondonating porphyrin and the electronaccepting fullerene moiety, respectively, at this level of theory. More specifically, semiempirical molecular orbital (MO) configuration interaction (CI) and unrestricted natural orbital (UNO)-CI methods shed light on the nature of the charge-transfer states and emphasize the importance of the close proximity of donor and acceptor for effective electron transfer

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields by discussing the hierarchical pathway from generating the dataset of reference calculations to the construction of the machine learning model, and the validation of the physics generated by the model. We will use the symmetrized gradient-domain machine learning (sGDML) framework due to its ability to reconstruct complex high-dimensional potential energy surfaces (PES) with high precision even when using just a few hundreds of molecular conformations for training. The data efficiency of the sGDML model allows using reference atomic forces computed with high-level wave-function-based approaches, such as the gold standard coupled-cluster method with single, double, and perturbative triple excitations (CCSD(T)). We demonstrate that the flexible nature of the sGDML framework captures local and non-local electronic interactions (e.g., H-bonding, lone pairs, steric repulsion, changes in hybridization states (e.g., sp2⇌sp3), n → π∗ interactions, and proton transfer) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML models trained for different molecular structures at different levels of theory (e.g., density functional theory and CCSD(T)) provides empirical evidence that a higher level of theory generates a smoother PES. Additionally, a careful analysis of molecular dynamics simulations yields new qualitative insights into dynamics and vibrational spectroscopy of small molecules close to spectroscopic accuracy