John Parkhill

A tractable and accurate electronic structure method for static correlations: the perfect hextuples model.

We present the next stage in a hierarchy of local approximations to complete active space self-consistent field (CASSCF) model in an active space of one active orbital per active electron based on the valence orbital-optimized coupled-cluster (VOO-CC) formalism. Following the perfect pairing (PP) model, which is exact for a single electron pair and extensive, and the perfect quadruples (PQ) model, which is exact for two pairs, we introduce the perfect hextuples (PH) model, which is exact for three pairs. PH is an approximation to the VOO-CC method truncated at hextuples containing all correlations between three electron pairs. While VOO-CCDTQ56 requires computational effort scaling with the 14th power of molecular size, PH requires only sixth power effort. Our implementation also introduces some techniques which reduce the scaling to fifth order and has been applied to active spaces roughly twice the size of the CASSCF limit without any symmetry. Because PH explicitly correlates up to six electrons at a time, it can faithfully model the static correlations of molecules with up to triple bonds in a size-consistent fashion and for organic reactions usually reproduces CASSCF with chemical accuracy. The convergence of the PP, PQ, and PH hierarchy is demonstrated on a variety of examples including symmetry breaking in benzene, the Cope rearrangement, the Bergman reaction, and the dissociation of fluorine.

A truncation hierarchy of coupled cluster models of strongly correlated systems based on perfect-pairing references: the singles+doubles models.

Paired, active-space treatments of static correlation are augmented with additional amplitudes to produce a hierarchy of parsimonious and efficient cluster truncations that approximate the total energy. The number of parameters introduced in these models grow with system size in a tractable way: two powers larger than the static correlation model it is built upon: for instance cubic for the models built on perfect pairing, fourth order for a perfect quadruples (PQ) reference, and fifth order for the models built on perfect hextuples. These methods are called singles+doubles (SD) corrections to perfect pairing, PQ, perfect hextuples, and two variants are explored. An implementation of the SD methods is compared to benchmark results for F(2) and H(2)O dissociation problems, the H(4) and H(8) model systems, and the insertion of beryllium into hydrogen. In the cases examined even the quartic number of parameters associated with PQSD is able to provide results which meaningfully improve on coupled-cluster singles doubles (CCSD) (which also has quartic amplitudes) and compete with existing multi-reference alternatives.

Near-Infrared Spectroscopy of Nitrogenated Polycyclic Aromatic Hydrocarbon Cations from 0.7 to 2.5 μm

The near-infrared (NIR) spectra and absolute band strengths of 10 nitrogenated polycyclic aromatic hydrocarbon (PANH) radical cations isolated in an argon matrix are presented and compared with the spectra of their parent polycyclic aromatic hydrocarbon (PAH) radical cations. The 0.7Y2.5 � m (14,500Y4000 cm � 1 ) spectrum for the open-shell cation forms of two nitrogenated anthracenes (C13H9N and C 12H8N2), four isomeric nitrogenated benzanthracenes (C17H11N), and four isomeric nitrogenated dibenzanthracenes (C21H13N) are reported. These ionized PANHs have allowed electronic transitions that give rise to strong absorption bands in the NIR. Low-lying excited states for these PANH ions are computed using time-dependent density functional theory (TDDFT). The resulting vertical excitation spectrum characterizes the transitions, and leads to a simple model that predicts the qualitative trends in absorption energy. The direction of the shift depends on the position of the nitrogen atom within the PANH and the relative magnitudes of the donor and acceptor molecular orbitals involved in the transitions. As with nonnitrogenated PAHs, ionized interstellar PANHs can be expected to contribute to the mid-IR emission features from UV-rich as well as UV-poor regions, and add weak, broad band structure to the NIR region of the interstellar extinction curve. Subject headingg astrochemistry — dust, extinction — ISM: general — ISM: molecules — molecular data

The many-body expansion combined with neural networks

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of calculations required for chemical accuracy is still prohibitively expensive for the ab initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab initio potential energy surfaces have improved, especially non-linear models based on neural networks (NNs) which can reproduce ab initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NNs and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size while reducing the scaling problem of large systems. NNs reduce, by a factor in excess of 106, the computational overhead of the MBE and reproduce the accuracy of ab initio calculations without specialized force fields. We show that for a small molecule extended system like methanol, accuracy can be achieved with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties. We also provide our open-source code for the neural network many-body expansion, Tensormol.

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, we present a neural network that predicts the energies of molecules as a sum of intrinsic bond energies. The network learns the total energies of the popular GDB9 database to a competitive MAE of 0.94 kcal/mol on molecules outside of its training set, is naturally linearly scaling, and applicable to molecules consisting of thousands of bonds. More importantly, it gives chemical insight into the relative strengths of bonds as a function of their molecular environment, despite only being trained on total energy information. We show that the network makes predictions of relative bond strengths in good agreement with measured trends and human predictions. A Bonds-in-Molecules Neural Network (BIM-NN) learns heuristic relative bond strengths like expert synthetic chemists, and compares well with ab initio bond order measures such as NBO analysis.

A sparse framework for the derivation and implementation of fermion algebra

Algorithms useful in the construction of electron correlation models are collected alongside new developments for cases of high rank and sparsity. In the first part of this paper a Brandow diagram manipulation program is presented. The complementary second section describes a general-rank sparse contraction algorithm which exploits the permutational symmetries of many-fermion quantities. Several recently published local correlation models (perfect quadruples and perfect hextuples) were built using these codes. This paper should facilitate reproduction and extension of high-rank electron correlation models that combine truncation by level of substitution with truncation by locality, such as the number of entangled electron pairs.

Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

Penalty functions for combining coupled-cluster and perturbation amplitudes in local correlation methods with optimized orbitals

Local active space correlation models based on the coupled-cluster doubles (CCD) model like Generalized Valence Bond Perfect Pairing (GVB-PP) and Imperfect Pairing (IP) are attractive methods for treating electron correlation, because they are computationally inexpensive and can describe strong correlations. However, they suffer from symmetry-breaking (SB) in systems with multiple resonance structures, which arises due to neglected correlations. We investigate the extent to which these problems can be removed by using second-order perturbation theory (PT) for weak correlations coupling three different electron pairs, and (infinite-order) coupled-cluster (CC) theory for stronger correlations involving electrons in only one or two pairs. The resulting Three-Pair Corrected Imperfect Pairing (TIP) method is explored here, and it is shown that to robustly combine CC and PT it is necessary to modify several aspects of the basic method. Most importantly, a penalty function term is introduced to ensure the PT amplitudes remain small. Comparison against CC treatment of the three-pair correlations suggests penalty terms will be beneficial for any hybrid CC/PT method that includes orbital optimization. The TIP method greatly reduces SB in aromatic hydrocarbons and recovers a significantly higher fraction of the valence electron correlation energy than IP.

Nonadiabatic Dynamics for Electrons at Second-Order: Real-Time TDDFT and OSCF2.

We develop a new model to simulate nonradiative relaxation and dephasing by combining real-time Hartree-Fock and density functional theory (DFT) with our recent open-systems theory of electronic dynamics. The approach has some key advantages: it has been systematically derived and properly relaxes noninteracting electrons to a Fermi-Dirac distribution. This paper combines the new dissipation theory with an atomistic, all-electron quantum chemistry code and an atom-centered model of the thermal environment. The environment is represented nonempirically and is dependent on molecular structure in a nonlocal way. A production quality, O(N(3)) closed-shell implementation of our theory applicable to realistic molecular systems is presented, including timing information. This scaling implies that the added cost of our nonadiabatic relaxation model, time-dependent open self-consistent field at second order (OSCF2), is computationally inexpensive, relative to adiabatic propagation of real-time time-dependent Hartree-Fock (TDHF) or time-dependent density functional theory (TDDFT). Details of the implementation and numerical algorithm, including factorization and efficiency, are discussed. We demonstrate that OSCF2 approaches the stationary self-consistent field (SCF) ground state when the gap is large relative to k(b)T. The code is used to calculate linear-response spectra including the effects of bath dynamics. Finally, we show how our theory of finite-temperature relaxation can be used to correct ground-state DFT calculations.

Cost-effective description of strong correlation: Efficient implementations of the perfect quadruples and perfect hextuples models

Novel implementations based on dense tensor storage are presented for the singlet-reference perfect quadruples (PQ) [J. A. Parkhill et al., J. Chem. Phys. 130, 084101 (2009)] and perfect hextuples (PH) [J. A. Parkhill and M. Head-Gordon, J. Chem. Phys. 133, 024103 (2010)] models. The methods are obtained as block decompositions of conventional coupled-cluster theory that are exact for four electrons in four orbitals (PQ) and six electrons in six orbitals (PH), but that can also be applied to much larger systems. PQ and PH have storage requirements that scale as the square, and as the cube of the number of active electrons, respectively, and exhibit quartic scaling of the computational effort for large systems. Applications of the new implementations are presented for full-valence calculations on linear polyenes (CnHn+2), which highlight the excellent computational scaling of the present implementations that can routinely handle active spaces of hundreds of electrons. The accuracy of the models is studied in the π space of the polyenes, in hydrogen chains (H50), and in the π space of polyacene molecules. In all cases, the results compare favorably to density matrix renormalization group values. With the novel implementation of PQ, active spaces of 140 electrons in 140 orbitals can be solved in a matter of minutes on a single core workstation, and the relatively low polynomial scaling means that very large systems are also accessible using parallel computing.