Organic semiconductors are exotic, carbon-based optoelectronic materials that are utilized in a broad spectrum of optoelectronic applications, stretching from organic light-emitting diodes to perovskite solar cells. They exhibit unique solid-state physical properties owing to the soft, van der Waals bonding between individual molecules. The presence of strong coupling between the electronic and structural dynamics gives rise to unique and fascinating phenomena in these molecular solids, such as a transient localization of the electronic states, but is also a performance-limiting factor for their charge transport and optoelectronic properties, such as carrier mobilities or excited-state lifetimes. Using a multiscale computational workflow, we aim to explore bulk charge transport parameters in organic semiconductors and procure molecule-specific quantitative predictions which would empower the prescreening of a diverse set of materials for the design of highly efficient optoelectronic devices.

Display and lighting technologies based on organic light-emitting diodes (OLEDs) have steadily increased in popularity, from the automotive industry to smartphones and televisions. Not only do they offer improved image quality via a greater contrast ratio when compared with the liquid crystal display technology, but the flexible and transparent possibilities of OLEDs make them an innovative choice in a competitive market. The performance of an OLED relies on an intricate balance between stability, efficiency, operational driving voltage, and color coordinates, with the aim of optimizing these parameters by employing an appropriate material design. Multiscale simulation techniques can aid with the rational design of these materials, to overcome existing shortcomings. For example, the obstacles surrounding blue OLEDs, in particular, the trade-off between stability and efficiency, while preserving blue emission.

One of the major advantages of using organic molecules is the possibility of manipulating chemical composition to target specific properties. However, when it comes to material design, it has been an expensive challenge for experimentalists to pre-screen potential candidates from a nearly infinite subset of organic compounds before synthesis. Therefore, to optimize design, a systematic approach that links chemical structure to macroscopic properties would be greatly beneficial. In this regard, exploring and screening a chemical space using high-throughput computing provides an alternative route. The strategy is to explore a diverse set of candidate structures through a layered, multiscale computational approach by understanding the structure-property relationship at each layer. The idea is to gain an intricate understanding of charge transport on a molecular level and insightful information on electronic functions. Subsequently, within a machine-learning framework, these results can be used to lead experimental studies building new organic materials to improve the overall performance of organic photovoltaic devices.

The accuracy of computational predictions is often governed by the employed atomistic model representing the underlying atomic interactions in the target compound. However, in many instances, the bottleneck has been the availability of sufficiently efficient interatomic potentials providing reliable energies and forces. Electronic structure methods can often accurately predict atomic interactions but are too computationally demanding to be applicable for large-scale systems. In contrast, empirical force fields are much faster to evaluate and can be used for large systems, but they often lack the accuracy needed for truly predictive simulations. The omnipresent issue of compromise between accuracy and speed can be solved by interatomic potentials developed by employing machine learning techniques. These potentials aim to provide the accuracy of electronic structure calculations at approximately the computational cost of force fields. Our objective is to build such machine-learning potential to perform material and reaction simulations which typically deal with slow chemical bond evolution in complex environments.

As global concerns about fossil-fuel depletion and greenhouse gas emissions continue to escalate, the demand for alternative energy from renewable and clean sources is rapidly increasing. The development of fuel cells and batteries may provide practical clean energy alternatives that can replace internal combustion engines in automobiles and also power personal electronics. However, despite decades of research, electrochemical energy conversion and storage technologies continue to lag behind fossil fuels in performance and cost. Fundamental problems that hinder the advancement in this area are primarily the lack of understanding of transport and catalytic mechanisms as well as the complexity of modeling chemical processes and dynamics at the interfaces between each component of electrochemical devices. Our goal is to develop computational tools based on multiscale theory to investigate and predict the behavior of complex systems that are relevant to renewable energy technologies. A key area of research is the study of proton and ion conduction mechanisms in electrolytes for batteries and fuel cells.