Combinatorial and High-Throughput Screening of Materials ...

ACS Combinatorial Science
REVIEW
Figure 7. D-Glyph plot of a catalyst library data. Four elements (Cr, Mn,
Co, and Te) are plotted on a 3D quaternary mixtures plot, while three
activities are mapped to three graphical attributes of a sphere glyph: color is
mapped to activities for acrolein, size is mapped to activities for 1,
5-hexadiene, and intensity is mapped to activities for acetone. The intensity
of the glyphs is adjusted on the left plot to accentuate the levels of acetone. 13
behavior can be modified by the effects of metal/metal or metal/
oxygen/metal interactions. In this respect, it is important to
know how to choose the “right” combination of metals. Just to
consider which combination is appropriate and why is in itself a
challenge, and hence when one considers mixed metal catalysts
with 4, 5, and more metals, we clearly have, what appears to be a
prohibitive problem on exploring in a reasonably timely manner
the catalytic behavior of such a vast array of chemistries.
The groups of Maier and Rajan 13,102,103 addressed this issue
through a study of 10 3 combinatorial catalyst library. The
chemistries sampled the complete composition spread of a five
dimensional search space containing the elements Cr, Co, Mn,
Mo, and Ni prepared by a sol gel recipe and tested by the
high-throughput screening reactor. To unravel the complexity of
these materials compositions, a visualization approach shown in
Figure 7 has been utilized to track the raw data from combinatorial
experiments. This visualization approach facilitated
the establishment of the framework on how data mining techniques
can be applied to unravel the complexity of combinatorial
experiments.
The further application of data mining techniques addressed
two questions, such as (1) the selectivity of a given metal to
catalytic activity (i.e., a reaction product) and (2) the relative
influence between metal chemistry and catalytic activity. The
loading plot of the developed PCA model provided the correlations
between the presence of a specific metal species and the
selectivity of a given reaction product (see Figure 8). The relative
correlations of different chemistries and their influence on
the selectivity of chemical reactions can be visualized through the
screening of the geometrical relationships between variables
represented in this loadings plot. In this case Mo or the pair
(Mo, Co) has positive correlations with acetone formation, while
Ni and Mn are tend to inhibit the formation of all the reaction
products, especially acetone. This example has shown that the
data mining techniques can not only help to identify the relative
Figure 8. Loadings plot of the developed PCA model derived from a
combinatorial library of multicomponent oxides (Mo, Ni, Mn, Cr, and
Co) and the selectivity of chemical products (acetone, propionic
adelhyde, acreolein, and 1,5-hexadiene). 18,97,103
selectivity of metal ions on catalytic products but also can help to
assess the relative impact of these elements.
Importantly, while PCA and other pattern recognition tools
are helpful in assessing the relative impact of multiple parameters
on materials properties, they are not predictive tools. For
predicting of materials properties, other methods should be
applied. 97 Just as informatics can aid in searching and discovering
critical data from combinatorial experiments, 71 76 it can also
serve as a predictive modeling tool based on CHT experimentation.
For instance, Li et al. have shown, 104 a new approach
involving a hybrid data mining to determine which aspects of a
polymeric molecular structure have the most impact on the
release kinetics behavior for drug delivery (see Figure 9). They
used these molecular descriptors to model the release profile of
proteins from a combinatorial library of biodegradable polymers.
The mathematical approach for modeling the release profile was
based on a combination of genetic algorithms (GA) and support
vector regression methods. The polymers explored from the
combinatorial libraries included linearly varying molar compositions
of binary formulations of copolymers, with the protein
release rate found to increase with decreasing polymer hydrophobicity.
From the new attribute selection approach, it was
determined that number of nonconjugated backbone COO
bonds, number of aromatic rings, and number of CH 2 bonds
were most important for accurately modeling the release kinetics
behavior. This work introduces a new approach for understanding
which factors control drug release and has important implications
for the rational design of new polymeric carriers for drug delivery
In addition to materials composition, materials microstructure
and processing parameters play equally important effects. Unraveling
the interactions between these effects is critical. Thus, informatics
techniques is applied to high-throughput screening data
from combinatorial synthesis methods. In one representative
example, the ability of a polymer system to both modulate its
effect on the immune response and to deliver a drug or antigen in a
controlled fashion provides an ideal platform for both vaccine
adjuvants and multicomponent implants. Many factors influence
the innate and adaptive immune responses, including the activation
588 dx.doi.org/10.1021/co200007w |ACS Comb. Sci. 2011, 13, 579–633