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Fabrication of Fully Solution Processed Inorganic Nanocrystal Photovoltaic Devices

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Published on: July 8, 2016

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV.

Elana Borvick¹, Assaf Y Anderson¹, Hannah-Noa Barad¹

¹Department of Chemistry, Institute for Nanotechnology & Advanced Materials, Bar Ilan University , Ramat-Gan 52900, Israel.

ACS Combinatorial Science

|November 10, 2017

Summary

This summary is machine-generated.

This study demonstrates using deposition parameters, not complex structural descriptors, to analyze photovoltaic material data. This approach successfully identified key parameters for fabricating higher-performing iron oxide solar cells.

Keywords:

data mining genetic programing iron-oxide photovoltaics

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Area of Science:

Materials Science
Data Science
Renewable Energy

Background:

Data mining tools are valuable for analyzing large material datasets from high-throughput methods.
Traditional structural descriptors for analysis are often difficult to obtain and optimize.
Developing efficient photovoltaic materials requires advanced analytical techniques.

Purpose of the Study:

To investigate the use of deposition process parameters as descriptors for analyzing photovoltaic data.
To demonstrate that deposition parameters can effectively model and predict photovoltaic performance.
To enable the discovery and fabrication of high-performance photovoltaic materials using data mining.

Main Methods:

Fabrication of iron oxide solar cell libraries using varied deposition parameters.
Measurement of photovoltaic performance for each fabricated cell.
Development of predictive models using genetic programming and stepwise regression with deposition parameters.

Main Results:

Models successfully identified critical deposition parameters for enhancing photovoltaic performance.
Fabrication of an iron oxide library based on model predictions yielded superior cell performance.
Demonstrated the efficacy of deposition parameters in predicting and optimizing solar cell efficiency.

Conclusions:

Deposition process parameters are effective descriptors for data mining in photovoltaics.
This method facilitates the discovery and fabrication of high-performance solar cells.
The approach offers a promising pathway for accelerating materials discovery in renewable energy.