Abstract
The Dalton Project provides a uniform platform access to the underlying full-fledged quantum chemistry codes Dalton and LSDalton as well as the PyFraME package for automatized fragmentation and parameterization of complex molecular environments. The platform is written in Python and defines a means for library communication and interaction. Intermediate data such as integrals are exposed to the platform and made accessible to the user in the form of NumPy arrays, and the resulting data are extracted, analyzed, and visualized. Complex computational protocols that may, for instance, arise due to a need for environment fragmentation and configuration-space sampling of biochemical systems are readily assisted by the platform. The platform is designed to host additional software libraries and will serve as a hub for future modular software development efforts in the distributed Dalton community.
Highlights
More than 20 years have passed since the first version of the Dalton program[1] was released as a result of merging the separate HERMIT, SIRIUS, ABACUS, and RESPONS codes that implemented one- and two-electron integrals, wavefunctions, energy derivatives, and response theory, respectively
Dalton provides a hierarchy of coupled cluster (CC) methods to model a variety of x-ray spectroscopies including near-edge x-ray absorption fine structure (NEXAFS),[54–57] photo-electron spectroscopy (PES),[56–59] transient x-ray absorption spectroscopy (TRXAS),[60,61] and resonant inelastic x-ray scattering (RIXS).[62]
The permanent charge distribution of the fragments in the environment is described by their full electronic densities, avoiding divergences of the multipole expansions, while still keeping the distributed polarizabilities to efficiently account for polarization effects
Summary
More than 20 years have passed since the first version of the Dalton program[1] was released as a result of merging the separate HERMIT, SIRIUS, ABACUS, and RESPONS codes that implemented one- and two-electron integrals, wavefunctions, energy derivatives, and response theory, respectively. The two layers interact by any one of three means of communication, namely, conventional file input/output (I/O), Python bindings, e.g., through CFFI (C Foreign Function Interface)[13] or pybind[11,14] or pure Python module import In this scheme, we view the Dalton and LSDalton executables as libraries serving the DP platform, and further modular library decomposition would be desirable, it is hampered by code legacy and entanglement. Without noticeable sacrifice in computational efficiency or program execution stability, the higher-level quantum chemical methods and iterative linear response equation solvers are implemented in Python with the use of NumPy and underlying threaded math kernel libraries With this as background, we have gained sufficient confidence to steer our project into a new direction as far as software engineering practices are concerned. IV, we present six concrete examples of DP platform runs before closing with an outlook into the future for the Dalton Project
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