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1. Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data

   https://doi.org/10.1002/aisy.202400310  

   Lee, Hoon Jeong; Chiu, Arlene; Lin, Yida; Chintapalli, Sreyas; Kamal, Serene; Ji, Eric; Thon, Susanna M (April 2025, Advanced Intelligent Systems)

   Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies. 

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2. Fabrication and study of H2O-incorporated zinc-phosphate films interfaced with organic dye and their photo-detection characteristics

   https://doi.org/10.1063/5.0269805  

   Spiegelhoff, Y; Sobolev, K; Kouklin, N (July 2025, Applied Physics Letters)

   This study explores the hydrothermal growth of zinc-phosphate-hydrate (ZPH) films and their application in dye-based photovoltaic sensors. ZPH films grown on electrically conductive ITO substrates exhibit a layered morphology with interlayer distances of ∼20–30 nm. Their light emission spectrum is dominated by a narrow UV band at 378 nm, with a full width at half maximum of 16 nm. Photoluminescence excitation data reveal a sharp cutoff edge at ∼447 nm, corresponding to a bandgap of 2.77 eV. When interfaced with anthocyanin dye, the ZPH films generated a photovoltage of up to 281 mV under white light illumination, with the electrolyte enhancing the rate of photogenerated charge transfer. Under repeated on/off illuminations and steady state, a zero-bias photo-to-dark current ratio approached 97.5, with photocurrent rise and decay constants of ∼0.4 and 10.8 s, respectively. These findings confirm that ZPH-based inorganic–organic interfaces, akin to conventional semiconductor junctions, hold strong potential for low-cost photovoltaics and self-powered UV–visible photodetectors operating in the sub-kHz frequency range. 

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3. Combining experiment and computation to elucidate the optical properties of Ce ³⁺ in Ba ₅ Si ₈ O ₂₁

   https://doi.org/10.1039/c9cp05576b  

   Zhong, Jiyou; Hariyani, Shruti; Zhuo, Ya; Zhao, Weiren; Liu, Xiang; Wen, Jun; Brgoch, Jakoah (January 2020, Physical Chemistry Chemical Physics)

   Complex alkaline earth silicates have been extensively studied as rare-earth substituted phosphor hosts for use in solid-state lighting. One of the biggest challenges facing the development of new phosphors is understanding the relationship between the observed optical properties and the crystal structure. Fortunately, recent improvements in characterization techniques combined with advances in computational methodologies provide the research tools necessary to conduct a comprehensive analysis of these systems. In this work, a new Ce 3+ substituted phosphor is developed using Ba 5 Si 8 O 21 as the host crystal structure. The compound is evaluated using a combination of experimental and computational methods and shows Ba 5 Si 8 O 21 :Ce 3+ adopts a monoclinic crystal structure that was confirmed through Rietveld refinement of high-resolution synchrotron powder X-ray diffraction data. Photoluminescence spectroscopy reveals a broad-band blue emission centered at ∼440 nm with an absolute quantum yield of ∼45% under ultraviolet light excitation ( λ ex = 340 nm). This phosphor also shows a minimal chromaticity-drift but with moderate thermal quenching of the emission peak at elevated temperatures. The modest optical response of this phase is believed to stem from a combination of intrinsic structural complexity and the formation of defects because of the aliovalent rare-earth substitution. Finally, computational modeling provides essential insight into the site preference and energy level distribution of Ce 3+ in this compound. These results highlight the importance of using experiment and computation in tandem to interpret the relationship between observed optical properties and the crystal structures of all rare-earth substituted complex phosphors. 

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4. Predicting Materials Parameters in Colloidal Quantum Dot Photovoltaic Devices Using Machine Learning Models Trained On Experimental Data

   https://doi.org/10.1109/PVSC48317.2022.9938867  

   Lee, Hoon Jeong; Hofelmann, Ariana B.; Lin, Yida; Thon, Susanna M. (June 2022, 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC))

   Numerous characterization techniques have been developed over the last century, which have advanced progress on the development of a variety of photovoltaic technologies. However, this multitude of techniques leads to increasing experimental costs and complexity. It would be useful to have an approach that does not require the time commitment or operation costs to directly learn and implement every new measurement technique. Herein, we explore several machine learning (ML) models that output complex materials parameters, such as electronic trap state density, solely using illuminated current-voltage curves. This greatly reduces both the complexity and cost of the characterization process. Current-voltage curves were chosen as the only input to our models because this type of measurement is relatively simple to perform and most photovoltaic research labs already collect this information on all devices. We compare several different ML network architectures, all of which are trained on experimental data from PbS colloidal quantum dot thin film solar cells. We predict values for underlying materials parameters and compare them to experimentally measured results. 

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5. Large-scale optical characterization of solid-state quantum emitters

   https://doi.org/10.1038/s41563-023-01644-8  

   Sutula, Madison; Christen, Ian; Bersin, Eric; Walsh, Michael P.; Chen, Kevin C.; Mallek, Justin; Melville, Alexander; Titze, Michael; Bielejec, Edward S.; Hamilton, Scott; et al (November 2023, Nature Materials)

   Solid-state quantum emitters have emerged as a leading quantum memory for quantum networking applications. However, standard optical characterization techniques are neither efficient nor repeatable at scale. Here we introduce and demonstrate spectroscopic techniques that enable large-scale, automated characterization of colour centres. We first demonstrate the ability to track colour centres by registering them to a fabricated machine-readable global coordinate system, enabling a systematic comparison of the same colour centre sites over many experiments. We then implement resonant photoluminescence excitation in a widefield cryogenic microscope to parallelize resonant spectroscopy, achieving two orders of magnitude speed-up over confocal microscopy. Finally, we demonstrate automated chip-scale characterization of colour centres and devices at room temperature, imaging thousands of microscope fields of view. These tools will enable the accelerated identification of useful quantum emitters at chip scale, enabling advances in scaling up colour centre platforms for quantum information applications, materials science and device design and characterization. 

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