A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and openshell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller-Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr 2 dimer, exploring zeolitecatalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.Keywords quantum chemistry, software, electronic structure theory, density functional theory, electron correlation, computational modelling, Q-Chem Disciplines Chemistry CommentsThis article is from Molecular Physics: An International Journal at the Interface Between Chemistry and Physics 113 (2015): 184, doi:10.1080/00268976.2014. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Authors 185A summary of the technical advances that are incorporated in the fourth major release of the Q-CHEM quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller-Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly corre...

The ab-initio density matrix renormalization group (DMRG) is a tool that can be applied to a wide variety of interesting problems in quantum chemistry. Here, we examine the density matrix renormalization group from the vantage point of the quantum chemistry user. What kinds of problems is the DMRG well-suited to? What are the largest systems that can be treated at practical cost? What sort of accuracies can be obtained, and how do we reason about the computational difficulty in different molecules? By examining a diverse benchmark set of molecules: π-electron systems, benchmark main-group and transition metal dimers, and the Mn-oxo-salen and Fe-porphine organometallic compounds, we provide some answers to these questions, and show how the density matrix renormalization group is used in practice. C 2015 AIP Publishing LLC. [http://dx

This perspective article introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments.CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource which is provided by distributed volunteer computing on IBM's World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure-property relations in the domain of organic electronics. Such insights can open the door to a rational and systematic design of future high-performance materials. The computational work in CEP is tightly embedded in a collaboration with experimentalists, who provide valuable input and feedback to the project.

The virtual high-throughput screening framework of the Harvard Clean Energy Project allows for the computational assessment of candidate structures for organic electronic materials -in particular photovoltaics -at an unprecedented scale. We report the most promising compounds that have emerged after studying 2.3 million molecular motifs by means of 150 million density functional theory calculations. Our top candidates are analyzed with respect to their structural makeup in order to identify important building blocks and extract design rules for efficient materials. An online database of the results is made available to the community.

In this perspective we explore the use of strategies from drug discovery, pattern recognition, and machine learning in the context of computational materials science. We focus our discussion on the development of donor materials for organic photovoltaics by means of a cheminformatics approach. These methods enable the development of models based on molecular descriptors that can be correlated to the important characteristics of the materials. Particularly, we formulate empirical models, parametrized using a training set of donor polymers with available experimental data, for the important current-voltage and efficiency characteristics of candidate molecules. The descriptors are readily computed which allows us to rapidly assess key quantities related to the performance of organic photovoltaics for many candidate molecules. As part of the Harvard Clean Energy Project, we use this approach to quickly obtain an initial ranking of its molecular library with 2.6 million candidate compounds. Our method reveals molecular motifs of particular interest, such as the benzothiadiazole and thienopyrrole moieties, which are present in the most promising set of molecules.

graphics card. Furthermore, speedups of other matrix algebra based electronic structure calculations are anticipated as a result of using a similar approach.

We study the effects of chemical bonding on Raman scattering from benzenethiol chemisorbed on silver clusters using time-dependent density functional theory (TDDFT). Raman scattering cross sections are computed using a formalism that employs analytical derivatives of frequency-dependent electronic polarizabilities, which treats both off-resonant and resonant enhancement within the same scheme. In the off-resonant regime, Raman scattering into molecular vibrational modes is enhanced by one order of magnitude and shows pronounced dependence on the orientation and the local symmetry of the molecule. Additional strong enhancement of the order of 10(2) arises from resonant transitions to mixed metal-molecular electronic states. The Raman enhancement is analyzed using Raman excitation profiles (REPs) for the range of excitation energies 1.6-3.0 eV, in which isolated benzenethiol does not have electronic transitions. The computed vibrational frequency shifts and relative Raman scattering cross sections of the metal-molecular complexes are in good agreement with experimental data on surface enhanced Raman scattering (SERS) from benzenethiol adsorbed on silver surfaces. Characterization and understanding of these effects, associated with chemical enhancement mechanism, may be used to improve the detection sensitivity in molecular Raman scattering.

Two new tools for the acceleration of computational chemistry codes using graphical processing units (GPUs) are presented. First, we propose a general black-box approach for the efficient GPU acceleration of matrix−matrix multiplications where the matrix size is too large for the whole computation to be held in the GPU’s onboard memory. Second, we show how to improve the accuracy of matrix multiplications when using only single-precision GPU devices by proposing a heterogeneous computing model, whereby single- and double-precision operations are evaluated in a mixed fashion on the GPU and central processing unit, respectively. The utility of the library is illustrated for quantum chemistry with application to the acceleration of resolution-of-the-identity second-order Møller−Plesset perturbation theory calculations for molecules, which we were previously unable to treat. In particular, for the 168-atom valinomycin molecule in a cc-pVDZ basis set, we observed speedups of 13.8, 7.8, and 10.1 times for single-, double- and mixed-precision general matrix multiply (SGEMM, DGEMM, and MGEMM), respectively. The corresponding errors in the correlation energy were reduced from −10.0 to −1.2 kcal mol−1 for SGEMM and MGEMM, respectively, while higher accuracy can be easily achieved with a different choice of cutoff parameter.