Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

Sun, Shijing; Hartono, Noor Titan Putri; Ren, Zekun; Oviedo, Felipe; Buscemi, Antonio M.; Layurova, Mariya; Chen, De Xin; Ogunfunmi, Tofunmi; Thapa, Janak; Ramasamy, Savitha; Settens, Charles; DeCost, Brian; Kusne, A. Gilad; Liu, Zhe; Tian, Siyu; Peters, Ian Marius; Correa‐Baena, Juan‐Pablo; Buonassisi, Tonio

doi:10.1016/j.joule.2019.05.014

Cited by 251 publications

(214 citation statements)

References 72 publications

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“…However, excitons rapidly dissociate in these materials, therefore this pathway is not expected to be efficient. Further investigation into Ruddlesden-Popper type perovskite-inspired materials [84][85][86] and less toxic double perovskites, combined with machine learning approaches [87][88][89][90] to find suitable materials has the potential to break the field wide open. In sensitized UC, not only the perovskite can be tuned, rather, we can also tune the energetics of the upconverting species.…”

Section: Plos Onementioning

confidence: 99%

Engineering 3D perovskites for photon interconversion applications

Nienhaus

2020

PLoS ONE

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In this review, we highlight the current advancements in the field of triplet sensitization by three-dimensional (3D) perovskite nanocrystals and bulk films. First introduced in 2017, 3D perovskite sensitized upconversion (UC) is a young fast-evolving field due to the tunability of the underlying perovskite material. By tuning the perovskite bandgap, visible-to-ultraviolet, near-infrared-to-visible or green-to-blue UC has been realized, which depicts the broad applicability of this material. As this research field is still in its infancy, we hope to stimulate the field by highlighting the advantages of these perovskite nanocrystals and bulk films, as well as shedding light onto the current drawbacks. In particular, the keywords toxicity, reproducibility and stability must be addressed prior to commercialization of the technology. If successful, photon interconversion is a means to increase the achievable efficiency of photovoltaic cells beyond its current limits by increasing the window of useable wavelengths.

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Section: Plos Onementioning

confidence: 99%

Engineering 3D perovskites for photon interconversion applications

Nienhaus

2020

PLoS ONE

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“…In contrast, recently, Bayesian inference coupled to a physics-based forward model and rapid light and temperature-dependent current-voltage measurements were shown to offer a statistically rigorous approach to identify the root cause(s) of underperformance in early-stage photovoltaic devices 13 . Furthermore, recently, the combination of physical insights with machine learning models have shown good promise in development of energy materials [14][15][16][17][18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

et al. 2020

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Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, without full transparency of the underlying physics, and with user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond global black-box 2 optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device-performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time-consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layerby-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.

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“…Therefore, another way to show the performance of BO is to see how many steps are required to select a fraction of targeted materials. According to a systematic comparison of theory and experiment, a decomposition energy (∆H d ) value of −29 meV per atom has been set as the lower limit for stable perovskites [62,71]. Our goal is to select several stable perovskites using fewer calculation steps.…”

Section: Resultsmentioning

confidence: 99%

Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites

Chen

Wang

et al. 2020

Sci. China Mater.

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The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality. In the field of photovoltaics, the bandgap (E g ) is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser limit. In this paper, the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability, optical absorption, and carrier lifetime. Multiple criteria make the quantitative description of material candidates difficult and computationally time-consuming. Taking halide perovskites as an example, we combine thermodynamic stability (ΔH d ) and E g into a unified figure of merit and use Bayesian optimization (BO) to accelerate materials screening. We have found that, in comparison to an exhaustive search via multiple parameters, BO based on the unified figure of merit can screen optimal candidates (E g,PBE between 0.6-1.2 eV, ΔH d >−29 meV per atom) more efficiently. Therefore, the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost.

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Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

Cited by 251 publications

References 72 publications

Engineering 3D perovskites for photon interconversion applications

Engineering 3D perovskites for photon interconversion applications

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites

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