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

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

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...

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Bibliographic Details
Main Authors: Hoon Jeong Lee, Arlene Chiu, Yida Lin, Sreyas Chintapalli, Serene Kamal, Eric Ji, Susanna M. Thon
Format: Article
Language:English
Published: Wiley 2025-04-01
Series:Advanced Intelligent Systems
Subjects:
machine learnings
materials parameter predictions
optoelectronics
PbS colloidal quantum dots
photovoltaics
Online Access:https://doi.org/10.1002/aisy.202400310
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https://doi.org/10.1002/aisy.202400310

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