arXiv subjects

Olga Wodo

Publications and source records attributed to Olga Wodo.

5 recordsLinked to original sources

arXiv2026

Predicting Organic Solar Cell Performance and Stability from Fast, Morphology-aware Current-Voltage Modeling

Understanding the relationship between morphology and performance in organic solar cells is essential for developing devices that are both high performing and resilient to aging. This work introduces a unique method capable of calculating the current-voltage (JV) curve of complex heterojunction morphologies containing up to five phases (donor amorphous, donor crystalline, acceptor amorphous, acceptor crystalline, mixed amorphous) with a very low computation time using morphology-aware descriptors of light absorption, exciton dissociation, non-geminate recombination and free charge carrier mobilities. The method is validated against Monte Carlo and 3D drift-diffusion simulations and applied to P3HT:PCBM and PM6:Y6 systems, shedding light on the physical compromises encountered to optimize device performance and lifetime. Finally, we show that the morphology-performance relationship is dependent on the materials system studied.

cond-mat.mtrl-sci↗

arXiv2025

Taxonomy of amorphous ternary phase diagrams: the importance of interaction parameters

Understanding phase diagrams is essential for material selection and design, as they provide a comprehensive representation of the thermodynamics of mixtures. This work delivers a broad and systematic overview of possible ternary phase diagrams for amorphous systems representative of polymers, small organic molecules, and solvents. Thanks to computationally efficient methods, an unprecedented library of $>$80,000 ternary phase diagrams is generated based on a systematic screening of interaction parameters. Twenty-one phase diagram types, including unreported ones, are identified. They are classified according to simple rules related to the number of immiscible material pairs, of miscibility gaps, and of three-phase regions. They are mapped onto the three-dimensional interaction parameters space, providing a clear picture of their likelihood and existence conditions. Four well-known phase-diagram types with 0, 1, 2, or 3 immiscible pairs are found to be the most likely. The numerous uncommon phase diagrams are mostly observed within a small parameter window around the critical interaction parameter values. For the most common phase diagram types, we show that the size of the processability window becomes sensitive to interaction parameter variations close to critical values. The sensitivity decreases for materials with increasing molar size. Finally, successful comparisons of simulated and experimental phase diagrams showcase the real-world relevance of this theoretical analysis. The presented results lay a robust foundation for rational design of solution processing conditions and for blend morphology control. Immediate applications include organic thin films and the identification of green solvents for sustainable processing.

cond-mat.mtrl-sci↗

arXiv2018

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. In this paper, we first show that off-the-shelf non-linear spectral dimensionality reduction methods, e.g., Isomap, fail for such data, primarily due to the presence of strong temporal correlations. Then, we propose a novel method, Entropy-Isomap, to address the issue. The proposed method is successfully applied to large data describing a fabrication process of organic materials. The resulting low-dimensional representation correctly captures process control variables, allows for low-dimensional visualization of the material morphology evolution, and provides key insights to improve the process.

stat.ML↗

arXiv2012

Modeling morphology evolution during solvent-based fabrication of organic solar cells

Solvent-based techniques usually involve preparing dilute blends of electron-donor and electron-acceptor materials dissolved in a volatile solvent. After some form of coating onto a substrate, the solvent evaporates. An initially homogeneous mixture separates into electron-acceptor rich and electron-donor rich regions as the solvent evaporates. Depending on the specifics of the blend and processing conditions different morphologies are typically formed. Experimental evidence consistently confirms that the morphology critically affects device performance. A computational framework that can predict morphology evolution can significantly augment experimental analysis. Such a framework will also allow high throughput analysis of the large phase space of processing parameters, thus yielding insight into the process-structure-property relationships. In this paper, we formulate a computational framework to predict evolution of morphology during solvent-based fabrication of organic thin films. This is accomplished by developing a phase field-based model of evaporation-induced and substrate-induced phase-separation in ternary systems. This formulation allows all the important physical phenomena affecting morphology evolution during fabrication to be naturally incorporated. We discuss the various numerical and computational challenges associated with a three dimensional, finite-element based, massively parallel implementation of this framework. This formulation allows, for the first time, to model 3D morphology evolution over large time spans on device scale domains. We illustrate this framework by investigating and quantifying the effect of various process and system variables on morphology evolution. We explore ways to control the morphology evolution by investigating different evaporation rates, blend ratios and interaction parameters between components.

cond-mat.mes-hall↗

arXiv2011

A novel graph-based formulation for characterizing morphology with application to organic solar cells

Organic solar cells have the potential for widespread usage due to their promise of low cost, roll-to-roll manufacturability, and mechanical flexibility. However, deployment is impeded by their relatively low power conversion efficiencies. The last decade has seen significant progress in enhancing the power conversion of these devices through various strategies. One such approach is based on morphology control. This is because morphology affects all phenomena involved in solar conversion: light absorption and electron-hole pair (exciton) generation; exciton diffusion and dissociation into free charges; and transport of charges to the electrodes. Progress in experimental characterization and computational modeling now allow reconstruction and imaging of the thin film morphology. This opens up the possibility of rationally linking fabrication with morphology, as well as morphology with performance. In this context, a comprehensive set of computational tools to rapidly quantify and classify the heterogeneous internal structure of thin films will be invaluable in linking process, structure and property. We present a novel graph-based framework to efficiently construct a broad suite of physically meaningful morphology descriptors. These morphology descriptors are further classified according to the physical subprocesses within an organic solar cells. The approach is motivated by the equivalence between a discretized morphology and a labeled, weighted, undirected graph. We utilize this approach to pose key questions related to structure characterization. We subsequently construct estimates and upper bounds of various efficiencies. The approach is showcased by characterizing the effect of thermal annealing on time-evolution of a thin film morphology. We conclude by formulating natural extensions to characterize crystallinity and anisotropy of the morphology using the framework.

cond-mat.mtrl-sci↗