Abstract:High-throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many technological applications. However, there are very few publications on the reproducibility of the HTE results obtained across different laboratories for the same materials system, and on the associated sample and data exchange standards. Here, we report a comparative study of Zn−Sn−Ti−O thin films materials using high-throughput experimental methods at
“…This experimental workflow at NREL has been benchmarked against other laboratories. 14 , 15 Other publications demonstrate the range of materials chemistries (e.g., oxides, 16 nitrides, 17 chalcogenides, 18 Li-containing materials, 19 intermetallics) 20 and properties (e.g., optoelectronic, 21 electronic, 22 piezoelectric, 23 photoelectrochemical, 24 thermochemical) 25 to which these HTE methods have been applied. Figure 2 Experimental and data workflows for high-throughput materials research The workflow starts with (A) experiment design, then material samples are (B) produced, (C) treated, (D) measured, and (E) stored in archives.…”
Section: Resultsmentioning
confidence: 99%
“…Information extracted from the raw files, including both open-source (e.g., ASCII, HDF) and reverse-engineered proprietary file formats (e.g., various .raw files), is placed into tables that correspond to the standard 4 × 11 library mapping grid from HTE studies at NREL, or other custom grids (e.g., 176-point grid used by National Institute of Standards and Technology). 14 Next, (2) the data extracted from the files are transformed from original (sometimes proprietary) to final format. The extraction method varies depending on the specific file type and, in some cases, utilizes additional Python libraries or programming languages (e.g., Ruby, C, R).…”
Section: Resultsmentioning
confidence: 99%
“…This experimental workflow at NREL has been benchmarked against other laboratories. 14,15 Other publications demonstrate the range of materials chemistries (e.g., oxides, 16 nitrides, 17 chalcogenides, 18 Li-containing materials, 19 intermetallics) 20 and properties (e.g., optoelectronic, 21 electronic, 22 piezoelectric, 23 photoelectrochemical, 24 thermochemical) 25 to which these HTE methods have been applied.…”
Summary
The High-Throughput Experimental Materials Database (HTEM-DB,
htem.nrel.gov
) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.
“…This experimental workflow at NREL has been benchmarked against other laboratories. 14 , 15 Other publications demonstrate the range of materials chemistries (e.g., oxides, 16 nitrides, 17 chalcogenides, 18 Li-containing materials, 19 intermetallics) 20 and properties (e.g., optoelectronic, 21 electronic, 22 piezoelectric, 23 photoelectrochemical, 24 thermochemical) 25 to which these HTE methods have been applied. Figure 2 Experimental and data workflows for high-throughput materials research The workflow starts with (A) experiment design, then material samples are (B) produced, (C) treated, (D) measured, and (E) stored in archives.…”
Section: Resultsmentioning
confidence: 99%
“…Information extracted from the raw files, including both open-source (e.g., ASCII, HDF) and reverse-engineered proprietary file formats (e.g., various .raw files), is placed into tables that correspond to the standard 4 × 11 library mapping grid from HTE studies at NREL, or other custom grids (e.g., 176-point grid used by National Institute of Standards and Technology). 14 Next, (2) the data extracted from the files are transformed from original (sometimes proprietary) to final format. The extraction method varies depending on the specific file type and, in some cases, utilizes additional Python libraries or programming languages (e.g., Ruby, C, R).…”
Section: Resultsmentioning
confidence: 99%
“…This experimental workflow at NREL has been benchmarked against other laboratories. 14,15 Other publications demonstrate the range of materials chemistries (e.g., oxides, 16 nitrides, 17 chalcogenides, 18 Li-containing materials, 19 intermetallics) 20 and properties (e.g., optoelectronic, 21 electronic, 22 piezoelectric, 23 photoelectrochemical, 24 thermochemical) 25 to which these HTE methods have been applied.…”
Summary
The High-Throughput Experimental Materials Database (HTEM-DB,
htem.nrel.gov
) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.
“…The Energy Materials Network (EMN) is a United States Department of Energy (DOE) and Office of Energy Efficiency and Renewable Energy (EERE) network of consortia formed to accelerate the process of materials discovery, characterization, scale-up, and commercial deployment focused on solving the nation's toughest materials challenges in the energy sector. Building on the working concepts of High-Throughput Experimentation [1] and the Materials Genome Initiative [2], the EMN was envisioned to be a coordinated resource network for advanced materials R&D, enabling industry and university access to world class materials capabilities at the National Labs. The concept of the EMN can be defined broadly as a virtual laboratory.…”
In early 2015 the United States Department of Energy conceived of a consortium of collaborative bodies based on shared expertise, data, and resources that could be targeted towards the more difficult problems in energy materials research. The concept of virtual laboratories had been envisioned and discussed earlier in the decade in response to the advent of the Materials Genome Initiative and similar scientific thrusts. To be effective, any virtual laboratory needed a robust method for data management, communication, security, data sharing, dissemination, and demonstration to work efficiently and effectively for groups of remote researchers. With the accessibility of new, easily deployed cloud technology and software frameworks, such individual elements could be integrated, and the required collaboration architecture is now possible. The developers have leveraged open-source software frameworks, customized them, and merged them into a platform to enable collaborative energy materials science, regardless of the geographic dispersal of the people and resources. After five years in operations, the systems are demonstratively an effective platform for enabling research within the Energy Material Networks (EMN). This paper will show the design and development of a secured scientific data sharing platform, the ability to customize the system to support diverse workflows, and examples of the enabled research and results connected with some of the Energy Material Networks.
“…While the plugins are generally useful regardless of the data source, the instruments included in COMBIgor are specific to NREL. However, some of these instruments are used at other laboratories that employ HTE methods [58][59][60][61]. Also included in the COMBIgor package are well-commented, generic examples…”
Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties.Due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large arXiv:1904.07989v2 [cond-mat.mtrl-sci] 30 Apr 2019 data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms.Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.
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