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

Abstract

Data mining tools have been known to be useful for analyzing large material data sets generated by high-throughput methods. Typically, the descriptors used for the analysis are structural descriptors, which can be difficult to obtain and to tune according to the results of the analysis. In this Research Article, we show the use of deposition process parameters as descriptors for analysis of a photovoltaics data set. To create a data set, solar cell libraries were fabricated using iron oxide as the absorber layer deposited using different deposition parameters, and the photovoltaic performance was measured. The data was then used to build models using genetic programing and stepwise regression. These models showed which deposition parameters should be used to get photovoltaic cells with higher performance. The iron oxide library fabricated based on the model predictions showed a higher performance than any of the previous libraries, which demonstrates that deposition process parameters can be used to model photovoltaic performance and lead to higher performing cells. This is a promising technique toward using data mining tools for discovery and fabrication of high performance photovoltaic materials.

Overcoming Phase‐Purity Challenges in Complex Metal Oxide Photoelectrodes: A Case Study of CuBi2O4

“…Therefore, designing highly controlled synthesis routes toward highly crystalline, single-phase materials will become more challenging as the number of elements comprises the photoabsorber would increase. [25,26] Physical synthesis approaches (i.e., pulsed laser deposition, molecular beam epitaxy) of high-quality metal-oxides thin films have been developed and continuously improved in the past three decades. [27] Common denominators in these methods are the use of lattice-matched substrates materials, which enable epitaxial growth, and the ability to deposit and/or post-anneal at high temperatures (≤1000 °C).…”

The dye-sensitized solar cell database

“…Given the demand for methodologies that can accelerate the design of molecular materials with tailored properties, cheminformatics (or materials informatics) based frameworks for high-throughput screening of candidate structures have been proposed: dyes [ 25 ], solid state metal oxide photovoltaic cells [ 26 ] and organic photovoltaics [ 27 , 28 ]. With a view to understanding how structural/chemical modifications impact the solar cell performances, recent efforts have focused on creating quantitative structure property relationships [ 26 , 29 – 36 ] that establish a mathematical relationship between various molecular structure descriptors and a solar cell property of interest such as the PCE. The models produced in this process have been further used to direct the search for promising dyes/photvoltaic materials that satisfy desirable criteria [ 26 , 37 – 39 ].…”

“…With a view to understanding how structural/chemical modifications impact the solar cell performances, recent efforts have focused on creating quantitative structure property relationships [ 26 , 29 – 36 ] that establish a mathematical relationship between various molecular structure descriptors and a solar cell property of interest such as the PCE. The models produced in this process have been further used to direct the search for promising dyes/photvoltaic materials that satisfy desirable criteria [ 26 , 37 – 39 ]. Informatics approaches have also been recently applied to the identification of suitable photocathode materials [ 40 ] and solid state electrolytes [ 41 ].…”

Polyelemental, Multicomponent Perovskite Semiconductor Libraries through Combinatorial Screening

“…Interestingly, this holistic approach uses "bad result" as inputs for a deeper understanding of the overall material group which can lead to new knowledge and eventually to materials with desired properties. [24][25][26] High throughput methods have been employed for example in the areas of catalysis, functional materials, industrial coating, sensing materials, pharmaceutical products or biomaterials. [25] One particularly interesting topic for this approach are ceramic oxide perovskites for piezoelectronics, for example PbZr 0.52 Ti 0.48 O 3 , that contain unwanted Pb.…”

“…This can also involve data mining to recognize patterns or anomalies to unearth predictive descriptors blending interdisciplinary fields such as statistics, machine learning or artificial intelligence. Interestingly, this holistic approach uses “bad result” as inputs for a deeper understanding of the overall material group which can lead to new knowledge and eventually to materials with desired properties …”