Combinatorial and High-Throughput Screening of Materials ...

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Table 3. Functions of Data Management System
function current capabilities needs
experimental planning
database
instrument control
data processing
data mining
• composition parameters
• process parameters
• library design
• entry and search of composition and process variables
• operation with heterogeneous data
• unification of numerical data between different instruments,
databases, individual computers
operation of diverse instruments
• visualization of compositions and process conditions
of library elements
• visualization of measured parameters
• matrix algebra
• cluster analysis
• quantification
• outlier detection
• multivariate processing of steady-state and time-resolved
data
• third party statistical packages
• prediction of properties of new materials
• virtual libraries
• cluster analysis
• molecular modeling
• QSAR
• identification of appropriate descriptors on different levels
(atomic, molecular, process, etc.)
• iterative intelligent experimental planning based on results from
virtual or experimental libraries
• storage and manipulation (search) of large amounts (tera/peta bytes
and more) of data
• development of advanced query systems to databases that can be
adapted to machine learning algorithms
• interinstrument calibration
• full instrument diagnostics
• plug-'n'-play multiple instrument configurations
• advanced data compression
• processing of large amounts (terabytes and more) of data
• linking of physical base computational tools to data processing
• scientific visualization tools
• establishment of nonlinear and hybrid data mining tools
• “smart” data mining algorithms that can identify the right type
of tools simply based on the survey of the data available
Figure 4E shows a 48-element array of sensor materials positioned
in a gas flow-cell for the monitoring of the materials response upon
exposure to vapors of interest. For the evaluation of sensing
materials, absorption spectra were collected over a period of time
of reaction of these sensing materials with a vapor of interest.
Results of these measurements are illustrated in Figure 4F. Other
examples of second-order systems applied for high-throughput
materials characterization are excitation emission luminescence
measurement systems, 65,66 GC-MS, 67,68 and others.
The increase in the measurement dimensionality (i.e., the
order of analytical instrumentation) improves the analytical
capabilities of the screening systems and makes possible their
use for reaction monitoring and optimization. These capabilities
include increased analyte selectivity, more simple approach to
reject contributions from interferences, multicomponent analysis,
and outlier detection. Importantly, second- and higher-order
measurement approaches benefit from the improved performance
even in presence of unknown interferences. 69
2.4. Data Analysis and Mining. The CHT experiments create
significant amount of data, generating challenges in data management.
The issues range from managing work flows in experiments,
to tracking multivariate measurements, to storing the
data, to be able to query and retrieve information from databases,
and to mine the appropriate descriptors to predict materials
properties. In an ideal CHT workflow, one should “analyze in a
day what is made in a day”. 70 Such performance depends on the
adequate data management capabilities of the CHT workflow.
Table 3 illustrates important functions of the data management
system and demonstrates aspects that have been already developed
and that are under development. Data management strategies
for different applications were summarized by Koinuma
and co-workers. 71,72 The aspects of information processing that
focus on the scientific interpretation of data generated from CHT
72 78
screening have been extensively discussed.
Data mining techniques have two primary functions such as
pattern recognition and prediction, both of which form the
foundations for understanding materials behavior. Following
the treatment of Tan et.al. 79 81 pattern recognition serves as a
basis for deriving correlations, trends, clusters, trajectory and
anomalies among disparate data. The interpretation of these
patterns is intrinsically tied to an understanding of materials
physics and chemistry. In many ways this role of data mining is
similar to the phenomenological structure property paradigms
that play a central role in the study of engineering materials. It is
585 dx.doi.org/10.1021/co200007w |ACS Comb. Sci. 2011, 13, 579–633