The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

We present a translation of the chemical intuition in materials discovery, in terms of chemical similarity of efficient materials, into a rigorous framework exploiting machine learning. We computed equilibrium geometries and electronic properties (DFT) for a database of 249 Organic donor–acceptor pairs. We obtain similarity metrics between pairs of donors in terms of electronic and structural parameters, and we use such metrics to predict photovoltaic efficiency through linear and non-linear machine learning models. We observe that using only electronic or structural parameters leads to similar results, while considering both parameters at the same time improves the predictive capability of the models up to correlations of r ≈ 0.7. Such correlation allows for reliable predictions of efficient materials, and lends to be coupled with combinatorial of evolutionary approaches for a more reliable virtual screening of candidate materials.

Designing molecular materials with very large exciton diffusion lengths would remove some of the intrinsic limitations of present-day organic optoelectronic devices. Yet, the nature of excitons in these materials is still not sufficiently well understood. Here we present Frenkel exciton surface hopping, an efficient method to propagate excitons through truly nano-scale materials by solving the time-dependent Schrödinger equation coupled to nuclear motion. We find a clear correlation between diffusion constant and quantum delocalization of the exciton. In materials featuring some of the highest diffusion lengths to date, e.g. the non-fullerene acceptor Y6, the exciton propagates via a transient delocalization mechanism, reminiscent to what was recently proposed for charge transport. Yet, the extent of delocalization is rather modest, even in Y6, and found to be limited by the relatively large exciton reorganization energy. On this basis we chart out a path for rationally improving exciton transport in organic optoelectronic materials.

A large set of candidates for singlet fission, one of the most promising processes able to improve the efficiency of solar cells, are identified by screening a database of known molecular materials.

High power conversion efficiency are displayed by organic solar cells with small energy gap between LUMO+1 and LUMO orbitals.

Molecules where the energy of the lowest excited singlet state is found below the energy of the lowest triplet state (inverted singlet-triplet molecules) are extremely rare. It is particularly challenging to discover new ones through virtual screening because the required wavefunction-based methods are expensive and unsuitable for high-throughput calculations. Here, we devised a virtual screening approach where the molecules to be considered with advanced methods are pre-selected with increasingly more sophisticated filters that include the evaluation of the HOMO-LUMO exchange integral and approximate CASSCF calculations. A final set of 7 candidates (0.05% of the initial 15 000) were verified to possess inversion between singlet and triplet states with state-of-the-art multireference methods (MS-CASPT2). One of them is deemed of particular interest because it is unrelated to other proposals made in the literature.