Search

Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the {\omega}B97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations., Comment: 19 pages, 6 figures

The Basis Set Exchange (BSE) has been a prominent fixture in the quantum chemistry community. First publicly available in 2007, it is recognized by both users and basis set creators as the de facto source for information related to basis sets. This popular resource has been rewritten, utilizing modern software design and best practices. The basis set data has been separated into a stand-alone library with an accessible API, and the Web site has been updated to use the current generation of web development libraries. The general layout and workflow of the Web site is preserved, while helpful features requested by the user community have been added. Overall, this design should increase adaptability and lend itself well into the future as a dependable resource for the computational chemistry community. This article will discuss the decision to rewrite the BSE, the new architecture and design, and the new features that have been added.

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry results.The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of quantum chemistry computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface.Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org.The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of quantum chemistry computations for individuals or groups of researchers at any scale.The QCArchive Infrastructure is open-source (BSD-3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda.

Psi4 is a free and open-source ab initio electronic structure program providing Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of Psi4’s core functionality via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSchema data format, facilitating interoperability. A rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCArchive Infrastructure project, make the latest version of Psi4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.

Psi4 is an ab initio electronic structure program providing methods such as Hartree-Fock, density functional theory, configuration interaction, and coupled-cluster theory. The 1.1 release represents a major update meant to automate complex tasks, such as geometry optimization using complete-basis-set extrapolation or focal-point methods. Conversion of the top-level code to a Python module means that Psi4 can now be used in complex workflows alongside other Python tools. Several new features have been added with the aid of libraries providing easy access to techniques such as density fitting, Cholesky decomposition, and Laplace denominators. The build system has been completely rewritten to simplify interoperability with independent, reusable software components for quantum chemistry. Finally, a wide range of new theoretical methods and analyses have been added to the code base, including functional-group and open-shell symmetry adapted perturbation theory (F-SAPT and O-SAPT), density-fitted coupled cluster with frozen natural orbitals [DF-FNO-CCSD(T)], orbital-optimized perturbation and coupled-cluster methods (e.g., OO-MP2 and OO-CCSD), density-fitted multiconfigurational self-consistent field (DF-MCSCF), density cumulant functional theory (DCT), algebraic-diagrammatic construction [ADC(2)] excited states, improvements to the geometry optimizer, and the “X2C” approach to relativistic corrections, among many other improvements.

Within computational chemistry, the NWChem package has arguably been the de facto standard for running high-accuracy numerical simulations on the most powerful supercomputers. In order to better address the challenges presented by emerging exascale architectures, the decision has been made to rewrite NWChem. Design of the resulting package, NWChemEx, has been driven by exascale computing; however, significant additional design considerations have arisen from the team's involvement with the Molecular Sciences Software Institute (MolSSI). MolSSI is a National Science Foundation initiative focused on establishing coding and data standards for the computational chemistry community. As a result, NWChemEx is built upon a general computational chemistry framework called the simulation development environment (SDE) that is designed with a focus on extensibility and interoperability. The present manuscript describes the modular approach of the SDE and how it has been used to implement the self-consistent field algorithm within NWChemEx. Public domain – authored by a U.S. government employee

We present a method for computing excitation energies for molecules in solvent, based on the combination of a minimal parameter implicit solvent model and the equation-of-motion coupled-cluster singles and doubles method (EOM-CCSD). In this method, the solvent medium is represented by a smoothly varying dielectric function, constructed directly from the quantum mechanical electronic density using only two tunable parameters. The solvent–solute electrostatic interactions are computed by numerical solution of the nonhomogeneous Poisson equation and incorporated at the Hartree–Fock stage of the EOM-CCSD calculation by modification of the electrostatic potential. We demonstrate the method by computing excited state transition energies and solvent shifts for several small molecules in water. Results are presented for solvated H2O, formaldehyde, acetone, and trans-acrolein, which have low-lying n → π* transitions and associated blue shifts in aqueous solution. Comparisons are made with experimental data and other theoretical approaches, including popular implicit solvation models and QM/MM methods. We find that our approach provides surprisingly good agreement with both experiment and the other models, despite its comparative simplicity. This approach only requires modification of the Fock operator and total energy expressions at the Hartree–Fock level—solvation effects enter into the EOM-CCSD calculation only through the Hartree–Fock orbitals. Our model provides a theoretically and computationally simple route for accurate simulations of excited state spectra of molecules in solution, paving the way for studies of larger and more complex molecules.

We present an efficient implementation of the Obara-Saika algorithm for the computation of electron repulsion integrals that utilizes vector intrinsics to calculate several primitive integrals concurrently in a SIMD vector. Initial benchmarks display a 2-4 times speedup with AVX instructions over comparable scalar code, depending on the basis set. Speedup over scalar code is found to be sensitive to the level of contraction of the basis set, and is best for (lAlB|lClD) quartets when lD = 0 or lB=lD=0, which makes such a vectorization scheme particularly suitable for density fitting. The basic Obara-Saika algorithm, how it is vectorized, and the performance bottlenecks are analyzed and discussed. © 2016 Wiley Periodicals, Inc.

We are introducing a versatile computerized approach to model and simulate polymer tacticities using seven single-stage statistical models. The theory behind the models, e.g., Bovey's versus Price's, Bernoullian, 1st or 2nd order Markovian, enantiomeric types, and combinations thereof is explained. One of the models, "E-B gen", which can be used to produce four types of enantiomorphically controlled tacticities, and the pentad distribution for the model "E-M1" are reported here for the first time. The relations of chain-end controlled models to binary copolymerizations are discussed in detail, and equations for the conversion of tacticity based probabilities to reactivity ratios to obtain related n-ad distributions are presented. The models were applied to 20 polymers with exemplary tacticities found in the literature. A related software program ("Polytact") based on Microsoft's Excel has been designed to calculate all relevant characteristics of the polymer tacticity and to present them in graphical form in a user-friendly manner. The program can be used to produce graphs of the triad, pentad and sequence length distributions and a simulation of 50 monomer repeat units in the polymer for each of the seven models. One of the main intended uses of the program is to compare the computed n-ad distributions to those of experimental polymers obtained from NMR spectroscopy and to gain insight into the polymerization mechanisms.