The role of machine learning in perovskite solar cell research

Chen, Chen; Maqsood, Ayman; Jacobsson, T. Jesper

doi:10.1016/j.jallcom.2023.170824

Cited by 13 publications

(6 citation statements)

References 155 publications

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“…To name a few, random forest, deep learning models, and support vector machines. [ 24,55 ] In this work, four methods were used for predicting the PCE: artificial neural network (ANN) with seven densely connected hidden layers and rectified linear unit activation function, [ 56 ] GP regressor, random forest, and XGBoost. Trained regression models were also used to generate dimension‐reduced manifolds for visualizing high‐dimensional data in this work.…”

Section: Methodsmentioning

confidence: 99%

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Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study

Tsoi,

Yerci

2024

Advcd Theory and Sims

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Machine learning (ML) is gaining more attention in photovoltaic research and will be a vital tool in reaching record‐high power conversion efficiencies (PCE) in the near future. One area, where ML is significantly beneficial is reducing the number of experiments needed to find the optimum combination of parameters in solar cell fabrication. Bayesian optimization (BO) provides routes for quickly identifying optimum parameters in problems with large parameter space. In this work, BO algorithms utilizing previous knowledge are demonstrated to result in faster optimization of tandem solar cells, a technology rapidly gaining more interest due to its potential to deliver record‐high performance. Namely, it is shown that in the space of all possible parameter combinations that take ≈88 years to evaluate, optimum PCE can be obtained in 20 min. Moreover, it is demonstrated that methods utilizing previous knowledge outperform those that do not by yielding an increase of ≈8.9%abs. in the 1st iteration and requiring 5× less time to reach a target PCE of 38.5% across 20 different trials. Results from this work help accelerate the development of tandem solar cells by removing the need for large numbers of experiments in identifying optimum parameters.

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Section: Methodsmentioning

confidence: 99%

“…As ML is gaining more attraction in the research of solar cells, [ 24–28 ] BO was also found to be useful. For example, Liu et al.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study

Tsoi,

Yerci

2024

Advcd Theory and Sims

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“…[ 37 , 38 , 39 ] What distinguishes the application of machine learning in the field of materials science from other domains is primarily the nature of the data collected and the specific features that are of importance. [ 40 , 41 , 42 , 43 , 44 ]…”

Section: The First Rung Up the Laddermentioning

confidence: 99%

The Future of Material Scientists in an Age of Artificial Intelligence

Maqsood,

Chen,

Jacobsson

2024

Advanced Science

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Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.

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“…With quick processing, vast datasets, future predictions, and ease of incorporation, ML offers a valuable tool for uncovering patterns and relationships within complex data, leading to new insights and breakthroughs in the field. 6,7…”

Section: Introductionmentioning

confidence: 99%