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.

Quantum chemistry embedding methods have become a popular approach to calculate molecular properties of larger systems. In order to account for finite temperature effects, including both configurational and conformational averaging, embedding methods are often combined with molecular dynamics (MD) simulations either in a direct or sequential manner. One of the decisive factors for a successful application of embedding methods is that that the underlying structures provided by the MD simulation are accurate, if not this will result in low-quality prediction of the molecular properties in question. Here we investigate different approaches for generating a set of molecular structures to be used in subsequent embedding calculations ranging from classical MD using a standard molecular mechanics (MM) force field to combined quantum mechanics/molecular mechanics (QM/MM) MD. Overall, we find an intermediate approach relying on classical MD followed by a constrained QM/MM geometry optimization to be a fairly accurate and very cost-effective approach, although this procedure naturally leads to underestimation of, for example, spectral bandwidths.

We present a modular open-source library for polarizable embedding (PE) named Cppe. The library is implemented in C++, and it additionally provides a Python interface for rapid prototyping and experimentation in a high-level scripting language. Our library integrates seamlessly with existing quantum chemical program packages through an intuitive and minimal interface. Until now, Cppe has been interfaced to three packages, Q-Chem, Psi4, and PySCF. Furthermore, we show Cppe in action using all three program packages for a computational spectroscopy application. With Cppe, host program interfaces only require minor programming effort, paving the way for new combined methodologies and broader availability of the PE model. 2

Linear response theory for the multiconfigurational short-range density functional theory (MC-srDFT) model is extended to triplet response with a singlet reference wave function. The triplet linear response equations for MC-srDFT are derived for a general hybrid srGGA functional and implemented in the Dalton program. Triplet excitation energies are benchmarked against the CC3 model of coupled cluster theory and the complete-active-space second-order perturbation theory using three different short-range functionals (srLDA, srPBE, and srPBE0), both with full linear response and employing the generalized Tamm-Dancoff approximation (gTDA). We find that using gTDA is required for obtaining reliable triplet excitations; for the CAS-srPBE model, the mean absolute deviation decreases from 0.40 eV to 0.26 eV, and for the CAS-srLDA model, it decreases from 0.29 eV to 0.21 eV. As expected, the CAS-srDFT model is found to be superior to the HF-srDFT model when analyzing the calculated triplet excitations for molecules in the benchmark set where increased static correlation is expected.

The fragment-based polarizable embedding (PE) model combined with an appropriate electronic-structure method constitutes a highly efficient and accurate multiscale approach for computing spectroscopic properties of a central moiety including effects from its molecular environment through an embedding potential. There is, however, a comparatively high computational overhead associated with the computation of the embedding potential which is derived from first principles calculations on individual fragments of the environment. To reduce the computational cost associated with the calculation of embedding-potential parameters, we developed a set of amino-acid-specific transferable parameters tailored for large-scale PE calculations that include proteins. The amino-acid-based parameters are obtained by simultaneously fitting to a set of reference electric potentials based on structures derived from a 1