Pavlo O. Dral

Quantum chemistry structures and properties of 134 kilo molecules

Computational de novo design of new drugs and materials requires rigorous and unbiased exploration of chemical compound space. However, large uncharted territories persist due to its size scaling combinatorially with molecular size. We report computed geometric, energetic, electronic, and thermodynamic properties for 134k stable small organic molecules made up of CHONF. These molecules correspond to the subset of all 133,885 species with up to nine heavy atoms (CONF) out of the GDB-17 chemical universe of 166 billion organic molecules. We report geometries minimal in energy, corresponding harmonic frequencies, dipole moments, polarizabilities, along with energies, enthalpies, and free energies of atomization. All properties were calculated at the B3LYP/6-31G(2df,p) level of quantum chemistry. Furthermore, for the predominant stoichiometry, C7H10O2, there are 6,095 constitutional isomers among the 134k molecules. We report energies, enthalpies, and free energies of atomization at the more accurate G4MP2 level of theory for all of them. As such, this data set provides quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

The semiempirical orthogonalization-corrected OMx methods (OM1, OM2, and OM3) go beyond the standard MNDO model by including additional interactions in the electronic structure calculation. When augmented with empirical dispersion corrections, the resulting OMx-Dn approaches offer a fast and robust treatment of noncovalent interactions. Here we evaluate the performance of the OMx and OMx-Dn methods for a variety of ground-state properties using a large and diverse collection of benchmark sets from the literature, with a total of 13035 original and derived reference data. Extensive comparisons are made with the results from established semiempirical methods (MNDO, AM1, PM3, PM6, and PM7) that also use the NDDO (neglect of diatomic differential overlap) integral approximation. Statistical evaluations show that the OMx and OMx-Dn methods outperform the other methods for most of the benchmark sets.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Theory, Implementation, and Parameters.

Semiempirical orthogonalization-corrected methods (OM1, OM2, and OM3) go beyond the standard MNDO model by explicitly including additional interactions into the Fock matrix in an approximate manner (Pauli repulsion, penetration effects, and core–valence interactions), which yields systematic improvements both for ground-state and excited-state properties. In this Article, we describe the underlying theoretical formalism of the OMx methods and their implementation in full detail, and we report all relevant OMx parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. For a standard set of mostly organic molecules commonly used in semiempirical method development, the OMx results are found to be superior to those from standard MNDO-type methods. Parametrized Grimme-type dispersion corrections can be added to OM2 and OM3 energies to provide a realistic treatment of noncovalent interaction energies, as demonstrated for the complexes in the S22 and S66×8 test sets.

Semiempirical UNO–CAS and UNO–CI: Method and Applications in Nanoelectronics

Unrestricted Natural Orbital-Complete Active Space Configuration Interaction, abbreviated as UNO-CAS, has been implemented for NDDO-based semiempirical molecular-orbital (MO) theory. A computationally more economic technique, UNO-CIS, in which we use a configuration interaction (CI) calculation with only single excitations (CIS) to calculate excited states, has also been implemented and tested. The class of techniques in which unrestricted natural orbitals (UNOs) are used as the reference for CI calculations is denoted UNO-CI. Semiempirical UNO-CI gives good results for the optical band gaps of organic semiconductors such as polyynes and polyacenes, which are promising materials for nanoelectronics. The results of these semiempirical UNO-CI techniques are generally in better agreement with experiment than those obtained with the corresponding conventional semiempirical CI methods and comparable to or better than those obtained with far more computationally expensive methods such as time-dependent density-functional theory. We also show that symmetry breaking in semiempirical UHF calculations is very useful for predicting the diradical character of organic compounds in the singlet spin state.

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.

Doped polycyclic aromatic hydrocarbons as building blocks for nanoelectronics: a theoretical study.

Density functional theory (DFT) and semiempirical UHF natural orbital configuration interaction (UNO-CI) calculations are used to investigate the effect of heteroatom substitution at the central position of a model polycyclic aromatic hydrocarbon. The effects of the substitution on structure, strain, electronic and spectral properties, and aromaticity of the compounds are discussed.

N -Heterotriangulene chromophores with 4-pyridyl anchors for dye-sensitized solar cells

A series of dimethylmethylene-bridged N-heterotriangulenes decorated with one, two, and three electron-withdrawing 4-pyridyls were synthesized. Their photophysical and electrochemical characteristics were examined and their successful application in n-type TiO2- and ZnO-based dye-sensitized solar cells demonstrated the ability of the 4-pyridyl moiety to act as an anchor.

Multiply bonded metal(II) acetate (rhodium, ruthenium, and molybdenum) complexes with the trans-1,2-bis(N-methylimidazol-2-yl)ethylene ligand.

The synthesis and structural characterization of new coordination polymers with the N,N-donor ligand trans-1,2-bis(N-methylimidazol-2-yl)ethylene (trans-bie) are reported. It was found that the acetate-bridged paddlewheel metal(II) complexes [M2(O2CCH3)4(trans-bie)]n with M = Rh, Ru, Mo, and Cr are linked by the trans-bie ligand to give a one-dimensional alternating chain. The metal-metal multiple bonds were analyzed with density functional theory and CASSCF/CASPT2 calculations (bond orders: Rh, 0.8; Ru, 1.7; Mo, 3.3).

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

We present an efficient approach for generating highly accurate molecular potential energy surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine learning (ML). We introduce structure-based sampling to automatically assign nuclear configurations from a pre-defined grid to the training and prediction sets, respectively. Accurate high-level ab initio energies are required only for the points in the training set, while the energies for the remaining points are provided by the ML model with negligible computational cost. The proposed sampling procedure is shown to be superior to random sampling and also eliminates the need for training several ML models. Self-correcting machine learning has been implemented such that each additional layer corrects errors from the previous layer. The performance of our approach is demonstrated in a case study on a published high-level ab initio PES of methyl chloride with 44 819 points. The ML model is trained on sets of different sizes and then used to predict the energies for tens of thousands of nuclear configurations within seconds. The resulting datasets are utilized in variational calculations of the vibrational energy levels of CH3Cl. By using both structure-based sampling and self-correction, the size of the training set can be kept small (e.g., 10% of the points) without any significant loss of accuracy. In ab initio rovibrational spectroscopy, it is thus possible to reduce the number of computationally costly electronic structure calculations through structure-based sampling and self-correcting KRR-based machine learning by up to 90%.