Open Source Electronic Structure Research Articles

GQCP: The Ghent Quantum Chemistry Package.

The Ghent Quantum Chemistry Package (GQCP) is an open-source electronic structure software package that aims to provide an intuitive and expressive software framework for electronic structure software development. Its high-level interfaces (accessible through C++ and Python) have been specifically designed to correspond to theoretical concepts, while retaining access to lower-level intermediates and allowing structural run-time modifications of quantum chemical solvers. GQCP focuses on providing quantum chemical method developers with the computational "building blocks" that allow them to flexibly develop proof of principle implementations for new methods and applications up to the level of two-component spinor bases.

Accurate frozen core approximation for all-electron density-functional theory.

We implement and benchmark the frozen core approximation, a technique commonly adopted in electronic structure theory to reduce the computational cost by means of mathematically fixing the chemically inactive core electron states. The accuracy and efficiency of this approach are well controlled by a single parameter, the number of frozen orbitals. Explicit corrections for the frozen core orbitals and the unfrozen valence orbitals are introduced, safeguarding against seemingly minor numerical deviations from the assumed orthonormality conditions of the basis functions. A speedup of over twofold can be achieved for the diagonalization step in all-electron density-functional theory simulations containing heavy elements, without any accuracy degradation in terms of the electron density, total energy, and atomic forces. This is demonstrated in a benchmark study covering 103 materials across the Periodic Table and a large-scale simulation of CsPbBr3 with 2560 atoms. Our study provides a rigorous benchmark of the precision of the frozen core approximation (sub-meV per atom for frozen core orbitals below -200 eV) for a wide range of test cases and for chemical elements ranging from Li to Po. The algorithms discussed here are implemented in the open-source Electronic Structure Infrastructure software package.

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations.

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

ET 1.0: An open source electronic structure program with emphasis on coupled cluster and multilevel methods.

The eT program is an open source electronic structure package with emphasis on coupled cluster and multilevel methods. It includes efficient spin adapted implementations of ground and excited singlet states, as well as equation of motion oscillator strengths, for CCS, CC2, CCSD, and CC3. Furthermore, eT provides unique capabilities such as multilevel Hartree-Fock and multilevel CC2, real-time propagation for CCS and CCSD, and efficient CC3 oscillator strengths. With a coupled cluster code based on an efficient Cholesky decomposition algorithm for the electronic repulsion integrals, eT has similar advantages as codes using density fitting, but with strict error control. Here, we present the main features of the program and demonstrate its performance through example calculations. Because of its availability, performance, and unique capabilities, we expect eT to become a valuable resource to the electronic structure community.

Performance analysis of open‐source distributed file systems for practical large‐scale molecular ab initio, density functional theory, and GW + BSE calculations

AbstractApplication of conventional post‐Hartree–Fock theory, density functional theory, and GW + BSE theory, on chemical and biological problems of growing molecular scale, has created a need for innovative designs in high‐performance computing infrastructures and associated middleware technologies. The growing trend toward petascale computing creates an enormous demand for highly efficient storage solutions. Notably, in reaching limitations of the scale‐up model, technological progress, and continual growth in demand has evolved into solutions that alternatively scale‐out. This investigation involves an analysis of commonly used open source storage solutions for high demand ab initio calculations. Comparisons are shown for the most competitive solutions for a broad range of workloads, following disk usage of key ab initio algorithms in the widely used open source electronic structure theory software, GAMESS. Assessment is based on results of read/write speeds and ratios, scaling ability, and impact of block‐size on overall performance of the distributed storage. GlusterFS is shown to generally outperform CEPH and local disk file systems, providing the most reasonable alternative to local storage, and is therefore recommended for large‐scale molecular calculations.

Amp: A modular approach to machine learning in atomistic simulations

Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning techniques can provide accurate potentials that can match the quality of electronic structure calculations, provided sufficient training data. These potentials can then be used to rapidly simulate large and long time-scale phenomena at similar quality to the parent electronic structure approach. Machine-learning potentials usually take a bias-free mathematical form and can be readily developed for a wide variety of systems. Electronic structure calculations have favorable properties–namely that they are noiseless and targeted training data can be produced on-demand–that make them particularly well-suited for machine learning. This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems. Potentials developed through the atom-centered approach are simultaneously applicable for systems with various sizes. Interpolation can be enhanced by introducing custom descriptors of the local environment. We demonstrate this in the current work for Gaussian-type, bispectrum, and Zernike-type descriptors. Amp has an intuitive and modular structure with an interface through the python scripting language yet has parallelizable fortran components for demanding tasks; it is designed to integrate closely with the widely used Atomic Simulation Environment (ASE), which makes it compatible with a wide variety of commercial and open-source electronic structure codes. We finally demonstrate that the neural network model inside Amp can accurately interpolate electronic structure energies as well as forces of thousands of multi-species atomic systems. Program summaryProgram title: AmpCatalogue identifier: AFAK_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AFAK_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: yesNo. of lines in distributed program, including test data, etc.: 21239No. of bytes in distributed program, including test data, etc.: 1412975Distribution format: tar.gzProgramming language: Python, Fortran.Computer: PC, Mac.Operating system: Linux, Mac, Windows.Has the code been vectorized or parallelized?: YesRAM: Variable, depending on the number and size of atomic systems.Classification: 16.1, 2.1.External routines: ASE, NumPy, SciPy, f2py, matplotlibNature of problem:Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms.Solution method:Machine learning provides flexible functional forms that can be improved as new situations are encountered. Typically, interatomic potentials yield from machine learning simultaneously apply to different system sizes.Unusual features:Amp is as modular as possible, providing a framework for the user to create atomic environment descriptor and regression model at will. Moreover, it has Atomic Simulation Environment (ASE) interface, facilitating interactive collaboration with other electronic structure calculators within ASE.Running time:Variable, depending on the number and size of atomic systems.