Visible‐light photocatalysis and electrocatalysis are two powerful strategies for the promotion of chemical reactions. Here, these two modalities are combined in an electrophotocatalytic oxidation platform. This chemistry employs a trisaminocyclopropenium (TAC) ion catalyst, which is electrochemically oxidized to form a cyclopropenium radical dication intermediate. The radical dication undergoes photoexcitation with visible light to produce an excited‐state species with oxidizing power (3.33 V vs. SCE) sufficient to oxidize benzene and halogenated benzenes via single‐electron transfer (SET), resulting in C−H/N−H coupling with azoles. A rationale for the strongly oxidizing behavior of the photoexcited species is provided, while the stability of the catalyst is rationalized by a particular conformation of the cis‐2,6‐dimethylpiperidine moieties.

The bond dissociation energies of a set of 44 3d transition metal-containing diatomics are computed with phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) utilizing a correlated sampling technique. We investigate molecules with H, N, O, F, Cl, and S ligands, including those in the 3dMLBE20 database first compiled by Truhlar and co-workers with calculated and experimental values that have since been revised by various groups. In order to make a direct comparison of the accuracy of our ph-AFQMC calculations with previously published results from 10 DFT functionals, CCSD(T), and icMR-CCSD(T), we establish an objective selection protocol which utilizes the most recent experimental results except for a few cases with well-specified discrepancies. With 1 arXiv:1901.09464v1 [physics.chem-ph] 27 Jan 2019 the remaining set of 41 molecules, we find that ph-AFQMC gives robust agreement with experiment superior to that of all other methods, with a mean absolute error (MAE) of 1.4(4) kcal/mol and maximum error of 3(3) kcal/mol (parenthesis account for reported experimental uncertainties and the statistical errors of our ph-AFQMC calculations).In comparison, CCSD(T) and B97, the best performing DFT functional considered here, have MAEs of 2.8 and 3.7 kcal/mol, respectively, and maximum errors in excess of 17 kcal/mol for both methods. While a larger and more diverse data set would be required to demonstrate that ph-AFQMC is truly a benchmark method for transition metal systems, our results indicate that the method has tremendous potential, exhibiting unprecedented consistency and accuracy compared to other approximate quantum chemical approaches.

Second order Møller-Plesset theory provides a remarkably simple form for the electron correlation energy with many desirable properties, e.g. it is size-consistent, free of self-interaction error, and scales with the fifth power of system size. However, MP2 exhibits well-known shortcomings including an incomplete description of dispersion interactions and sizable failures for transition metal chemistry. Herein, we first explore multiple physically justified forms of single-parameter regularization and then demonstrate that with appropriate parameter choice, regularized MP2 with Hartree-Fock reference orbitals yields high and transferable accuracy across a wide variety of noncovalent interactions (S22, S66, XB40, A24, and L7 test sets) and (mostly closedshell) transition metal thermochemistry (metal-carbonyl dissociations and a subset of MOR41). We find that, especially for systems with interacting π systems relevant to dispersion interactions and dative bonding, regularization serves to damp overestimated pair-wise additive contributions to the first-order amplitudes that affect correlation energy and charge-density. The optimal parameter values for the noncovalent and transition metal sets are 1.1 and 0.4 for two regularizers, κ and σ 2 , respectively. These two regularizers slightly degrade the accuracy of conventional MP2 for some small-molecule test sets which are well-known to be sensitive to charge-density distribution (radical stabilization energies, barrier heights, dipole moments, and polarizabilities), most of which have relatively large gaps. Due to the relatively straightforward implementations of nuclear gradient and other properties, we recommend κ-MP2 with κ = 1.1 as a more accurate alternative to conventional MP2 and other related variants. Our results suggest that appropriately regularized MP2 models represent promising forms for the nonlocal correlation part of double hybrid density functionals, at no additional cost over conventional MP2.

We present an implementation of phaseless Auxiliary-Field Quantum Monte Carlo (ph-AFQMC) utilizing graphical processing units (GPUs). The AFQMC method is recast in terms of matrix operations which are spread across thousands of processing cores and are executed in batches using custom Compute Unified Device Architecture kernels and the GPU-optimized cuBLAS matrix library. Algorithmic advances include a batched Sherman-Morrison-Woodbury algorithm to quickly update matrix determinants and inverses, density-fitting of the two-electron integrals, an energy algorithm involving a high-dimensional precomputed tensor, and the use of single-precision floating point arithmetic. These strategies accelerate ph-AFQMC calculations with both single- and multideterminant trial wave functions, though particularly dramatic wall-time reductions are achieved for the latter. For typical calculations we find speed-ups of roughly 2 orders of magnitude using just a single GPU card compared to a single modern CPU core. Furthermore, we achieve near-unity parallel efficiency using 8 GPU cards on a single node and can reach moderate system sizes via a local memory-slicing approach. We illustrate the robustness of our implementation on hydrogen chains of increasing length and through the calculation of all-electron ionization potentials of the first-row transition metal atoms. We compare long imaginary-time calculations utilizing a population control algorithm with our previously published correlated sampling approach and show that the latter improves not only the efficiency but also the accuracy of the computed ionization potentials. Taken together, the GPU implementation combined with correlated sampling provides a compelling computational method that will broaden the application of ph-AFQMC to the description of realistic correlated electronic systems.

The exact and phaseless variants of Auxiliary-Field Quantum Monte Carlo (AFQMC) have been shown to be capable of producing accurate ground-state energies for a wide variety of systems including those which exhibit substantial electron correlation effects. The computational cost of performing these calculations has to date been relatively high, impeding many important applications of these approaches. Here we present a correlated sampling methodology for AFQMC which relies on error cancellation to dramatically accelerate the calculation of energy differences of relevance to chemical transformations. In particular, we show that our correlated sampling-based AFQMC approach is capable of calculating redox properties, deprotonation free-energies, and hydrogen abstraction energies in an efficient manner without sacrificing accuracy. We validate the computational protocol by calculating the ionization potentials and electron affinities of the atoms contained in the G2 Test Set, and then proceed to utilize a composite method, which treats fixedgeometry processes with correlated sampling-based AFQMC and relaxation energies via MP2, to compute the ionization potential, deprotonation free-energy, and the O-H bond disocciation energy of methanol, all to within chemical accuracy. We show that the efficiency of correlated sampling relative to uncorrelated calculations increases with system and basis set size, and that correlated sampling greatly reduces the required number of random walkers to achieve a target statistical error. This translates to CPU-time speed-up factors of 55, 25, and 24 for the the ionization potential of the K atom, the deprotonation of methanol, and hydrogen abstraction from the O-H bond of methanol, respectively. We conclude with a discussion of further efficiency improvements that may open the door to the accurate description of chemical processes in complex systems.