Indoor light harvesters enable machine learning on fully autonomous IoT devices at 2.72 × 1015 photons per inference.

Abstract:In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor.

Gaining insight into structure−property relations is a key factor for the development of organic electronics. We present a multiscale framework for charge carrier mobilities in organic thin films empowered by machine-learned charge transfer integrals. The choice of the molecular representation is crucial for accurate and sensitive predictions. Using pentacene thin films, we investigate kernel based algorithms and systematically compare representations ranging from system-specific geometric to Coulomb matrix features to predict absolute and logarithmic transfer integrals. We use the predicted transfer integrals to compute the mobility, including its anisotropy, and compare it to reference values. Best accuracies were obtained by models using the interaction part of the Coulomb matrix as a feature and the logarithm of the transfer integral as a target. We achieve R 2 values of 0.97 for transfer integrals within an extensive range of 20 orders of magnitude and less than 27% error in the mobility. We show the transferability of the CIP feature for tetracene and DNTT with excellent prediction accuracies. Furthermore, we demonstrate that the interaction part of the CM successfully encodes the molecular identity and provides a highly sensitive ML framework. The presented framework opens the possibility for highly accurate mesoscopic transport simulations saving orders of magnitude in computational cost.

IoT devices powered by copper electrolyte-based dye-sensitized photovoltaic cells as ambient light harvesters achieve 38% power conversion efficiency and incorporate a dynamic intelligent on-device energy management system.

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