Table Of Content11
CHAPTER
Materials Informatics: An
Introduction
Krishna Rajan
Dept.ofMaterialsScience&Eng.andBioinformatics&ComputationalBiologyProgram(cid:1)
IowaStateUniversity,AmesIA,USA
1. THE WHAT AND WHY OF INFORMATICS
The searchfor neworalternative materials or processing strategies, whether
through experiment or simulation, has been a slowand arduous task, punc-
tuated by infrequent and often unexpected discoveries. Each of these find-
ings prompts a flurry of studies to better understand the underlying science
governing the behavior of these materials. While informatics is well estab-
lished in fields such as biology, drug discovery, astronomy and quantitative
social sciences, materials informaticsis still in its infancy.The few systematic
efforts that have been made to analyze trends in data as a basis for predic-
tions have in large part been inconclusive, not least of which is due to the
lack of large amounts of organized data and even more importantly the
challenge of sifting through them in a timelyandefficientmanner.
When combined with a huge combinatorial space of chemistries as
defined byeven a small portion of the periodic table, it is clearly seen that
searching for new materials with tailored properties is a prohibitive task.
Hence the search for new materials for new applications is limited to
educated guesses. Data that does exist is often limited to small regions of
compositional space. Experimental data is dispersed in the literature and
computationally derived data is limited to a few systems for which reliable
data exists for calculations. Even with recent advances in high-speed com-
puting, there are limits to how the structure and properties of many new
materials can be calculated. Hence this poses both a challenge and an
opportunity. The challenge is to deal with extremely large disparate data-
bases and large-scale computation. It is here that knowledge discovery in
databases or data mining, an interdisciplinary field merging ideas from sta-
tistics, machine learning, databases, and parallel and distributed comput-
ing, provides a unique tool to integrate scientific information and theory
InformaticsforMaterialsScienceandEngineering ©2013ElsevierInc.
DOI:http://dx.doi.org/10.1016/B978-0-12-394399-6.00001-1 Allrightsreserved. 1
2 KrishnaRajan
for materials discovery. The key challenge in data mining is the extraction
of knowledge and insight from massive databases. It takes the form of
discovering new patterns or building models from a given data set. The
challenge is to take advantage of recent advances in data mining and apply
them to state-of-the-art computational and experimental approaches for
materials discovery.
A complete materials informatics program will have an information
technology (IT)-based component that is linked to classical materials sci-
ence research strategies. The former includes a number of features that
help informatics to be critical in materials research (Figure 1.1):
(cid:129) Data warehousing and data management: This involves a science-based
selectionandorganizationofdatathatislinkedtoareliabledatasearch-
ingandmanagementsystem.
(cid:129) Data mining: Providing an accelerated analysis of large multivariate
correlations.
(cid:129) Scientificvisualization:Akeyareaofscientificresearch thatallowshigh
dimensionalinformationtobeassessed.
Materials science platform:
Structural properties at different
length/time scales (cid:129) Material theory
(cid:129) Materials processing
Material development
(cid:129) Property measurement
Material properties (cid:129) Computational techniques
(cid:129) Materials characterization
Microstructural evolution (cid:129) Discrete/atomistic modeling
(cid:129) Continuum modeling
Component design
System design
Data “fusion” through informatics
IT platform:
(cid:129) Ultra large scale databases of appropriate
information and data (data warehousing)
(cid:129) Scientific visualization
(cid:129) Data mining
(cid:129) Remote access/distributed computing
Figure 1.1 The role of materialsinformatics is pervasive across all aspects of materi-
als science and engineering. The mathematical tools based on data mining provide
thecomputationalengineforintegratingmaterialsscienceinformationacrosslength
scales. Informatics provides an accelerated means of fusing data and recognizing in
a rapid yet robust manner structure(cid:1)property relationships between disparate
lengthandtimescales.(FromRajan,2005.)
MaterialsInformatics:AnIntroduction 3
(cid:129) Cyber infrastructure: An information technology infrastructure that
can accelerate the sharing of information, data, and most importantly
knowledge discovery.
2. LEARNING FROM SYSTEMS BIOLOGY: AN “OMICS”
APPROACH TO MATERIALS DESIGN
The concept of complexity in biologyand how to assess the links between
information at the molecular level to that at the living organism level (e.g.
genomics, proteomics, etc.) is the foundation of systems biology. The
understanding of systems biology provides an excellent paradigm for the
materials scientist. Ultimately one would like to take an “atoms applica-
tions” approach to materials design. How dowe organize atoms and build
systematically structural units at increasing length scales to ultimately the
final engineering component or structure? At present we need to rely on
extensive prior knowledgewith experiments, computation, and ultimately
even failure analysis to understand the complex network of interactions of
materials behavior that govern the performance of an engineering system.
The problem is that even with advanced experimental and computational
tools, the rate of discovery is still slow, only punctuated by unexpected
findings (e.g. superconducting ceramics, conducting polymers) that stimu-
late new areas of research and development. The iterative approach as
showninFigure1.2iscommontomanyfieldsasonetriestolinkobserva-
tionswithmodels.Thechallengeistodevelopmodelsthatcapturethesys-
tem behavior by accounting for all the different levels of information that
contributetothesystem’sbehavior.
Biological Observed
system behavior
System
System
identification
simulation
(induction)
(deduction)
Model
Figure 1.2 Logic of information flow and and knowledge discovery in classical
research methodology. The example provided here addresses the use of qualitative
reasoningtosimulateandidentifymetabolicpathways.(FromKingetal.,2005.)
4 KrishnaRajan
The goal of modern systems biology is to understand physiology and
disease from the level of molecular pathways, regulatory networks, cells,
tissues, organs and ultimately the whole organism (Butcher et al., 2004).
As currently employed, the term “systems biology” encompasses many
different approaches and models for probing and understanding biological
complexity, and studies of many organisms from bacteria to man. A
similar paradigm exists for materials, e.g. atoms to airplanes (Figure 1.3).
Figure 1.3 Comparison of length scale challenges in designing drugs for the life
sciences (a; from Butcher et al., 2004) and designing materials for the engineering
sciences(b;fromNooretal.,2000).Notetheoverlapinthelengthscalesthatgovern
engineeringdesign/humanbody.
MaterialsInformatics:AnIntroduction 5
As aptly described by Butcher et al., the “omics” (the bottom-up
approach)focusesontheidentificationandglobalmeasurementofmolecular
components. Modeling (the top-down approach) attempts to form integra-
tive (across scales) models of human physiology and disease although, with
current technologies, such modeling focuses on relatively specific questions
at particular scales, e.g. at the pathway or organ levels. An intermediate
approach, with the potential to bridge the two, is to generate profiling data
from high-throughputexperimentsdesignedtoincorporatebiologicalcom-
plexity at multiple levels: multiple interacting active pathways, multiple
intercommunicatingcelltypes,andmultipledifferentenvironments.
A similar challenge occurs in materials science, identifying pathways
of how chemistry, crystal structure, microstructure, processing variables,
and component design and manufacturing “communicate” with each
other to ultimately define performance. This forms the materials science
equivalent of the biological regulatory network (Figure 1.4).
Because biological complexity is an exponential function of the num-
ber of system components and the interactions between them, and esca-
lates at each additional level of organization (Figure 1.5), such efforts are
currently limited to simple organisms or to specific minimal pathways
(and generally in very specific cell and environmental contexts) in higher
organisms. The same can be said of complexity in materials science.
Even if our ability to measure molecules and their functional states
and interactions were adequate to the task, computational limitations
alone would prohibit our understanding of cell and tissue behavior at the
molecular level. Thus, methodologies that filter information for rele-
vance, such as biological context and experimental knowledge of cellular
and higher level system responses, will be critical for successful under-
standing of different levels of organization in systems biology research.
Informatics is the enabling tool to facilitate this process. For instance,
Csete and Doyle (2002) have provided a very appropriate analog between
biological and engineering systems that can help to put materials infor-
matics in perspective. A striking example of converging information
across length scales is shown in Figure 1.6, where one is comparing
cruise speed to mass M over 12 orders of magnitude, from 747 and 777,
to fruit flies. This provides a good example of how, if one can integrate
and identify key metrics (data), functional relations between variables
across many scales can be developed. Here, a well-known elementary
argument shows good correspondence with the data and yields explana-
tions for deviations.
6 KrishnaRajan
Figure 1.4 An example of a “regulatory network” linking diverse sets of information
from both theory and experiment in the study of materials degradation due to
irradiation.(FromWirthetal.,2001.)
Such theories are largely irrelevant to complexity directly, but an
understanding of them leads to what is relevant. The scaling theory
described by the above figure does not distinguish between flight in the
atmosphere and in a laboratory wind tunnel. In the latter context, a much
simpler “mutant” 777 with nearly all of its 150,000-count “aeronome”
knockedout would have roughly the same lift, mass,and cruise speed, and
thus (from an allometric scaling viewpoint) would exhibit no deleterious
laboratory “phenotype”. Redundancy does not explain this finding.
Rather, the mutant has lost control systems and robustness required for
MaterialsInformatics:AnIntroduction 7
High scoring subnetwork
1 2 3 4 5
z score
<0 1 2 3 45 6 789>10
GAL80
Figure 1.5 Identification of regulatory pathways using network graphing and simu-
lated annealing methods. (This figure has been reprinted from Ideker et al., 2002, by
permissionofOxfordUniversityPress,andAitchisonandGalitski,2003.)
Figure 1.6 A scaling diagram of design behavior or aeronautical systems (both bio-
logicalandartificial).(AdaptedfromCseteandDoyle,2002.)
real flight outside the lab. Allometric scaling emphasizes the essential simi-
larities between these 777 variants and a toy scale model (and a fruit fly),
whereas our interest is in their huge differences in complexity. Similarly,
minimal cellular life requires a few hundred genes, yet even Escherichia coli
8 KrishnaRajan
have B4000 genes, less than 300 of which have been classified as “essen-
tial”. The likely reason for this “excess” complexity is also the presence of
complex regulatory networks for robustness. In technology as well as in
organisms, such robustness tradeoffs drive the evolution of spiraling
complexity.
3. WHERE DO WE GET THE INFORMATION?
One may naturally assume that having large amounts of data is critical for
any serious informatics studies. What constitutes “enough” data in mate-
rials science applications, however, can vary significantly. In studying
structural ceramics, for instance, fracture toughness measurements are dif-
ficult to make and in some of the more complex materials just a few care-
ful measurements can be of great value. Similarly, having reliable
measurements on fundamental constants or properties for a given material
involves very detailed measurement and/or computational techniques. In
essence data sets in materials science fall into two broad categories. The
first is data sets on a given material’s behavior as related to mechanical or
physical properties. The other is data sets related to intrinsic information
based on the chemical characteristic of the material, such as thermody-
namicdatasets.
In the materials science community, crystallographic and thermo-
chemical databases historically have been two of the best established in the
community. The former serves as the foundation for interpreting crystal
structure data of metals, alloys, and inorganic materials. The latter involves
the compilation of fundamental thermochemical information in terms of
heat capacity and calorimetric data. While crystallographic databases are
primarily used as a reference source, thermodynamic databases were actu-
ally one of the earliest examples of informatics, as these databases were
integrated into thermochemical computations to map phase stability in
binary and ternary alloys. This led to the development of computationally
derived phase diagrams, which is a classic example of integrating informa-
tion in databases to data models. The evolution of both databases has
occurredindependently,although intermsof their scientificvaluetheyare
extraordinarily intertwined. Phase diagrams map out regimes of crystal
structure in temperature(cid:1)composition space or temperature(cid:1)pressure
space. However, crystal structure databases have been developed totally
independently. At present the community has to work with each database
separately and information searches are cumbersome and data analysis
MaterialsInformatics:AnIntroduction 9
Figure 1.7 Example of integrating databases into knowledge discovery in drug dis-
covery from information at the genomic level. (Adapted from Strausberg and
Schreiber,2003.)
interpretation involving both is very difficult. Researchers only integrate
such information independently for a very specific system at a time based
on their individual interests. Hence there is at present no unified way to
explore patterns of behavior across both databases, which are scientifically
related. Examplesdoexist inthebiologicalandchemical sciencesandpro-
videusefultemplatesfor materialsscience(Figure1.7).
One of the more systematic efforts to address this challenge has been
that of Ashby (see, for example, Figure 1.8, Ashby, 2011), who showed
how, by merging phenomenological relationships in materials properties
with discrete data on specific material characteristics, one can begin to
developpatternsofclassification ofmaterialsbehavior.Thevisualization of
multivariate data was managed by using normalization schemes, which
permitted the development of “maps” that, in turn, provided a means of
capturing new means of clustering of materials properties. It also provided
a methodology to establish common structure(cid:1)property relationships
across seemingly different classes of materials. This approach, while very
valuable, is limited in its predictive value and is ultimately based on utiliz-
ingprior modelstobuildandseekrelationships.Inthe“informatics”strat-
egy of studying materials behavior we are approaching it from a broader
perspective.Byexploringalltypesofdatathatmayhavevaryingdegreesof
influence on a given property (or properties) with no prior assumptions,
oneutilizesdata-miningtechniquestoestablishbothclassification andpre-
dictive assessments in materials behavior. However, this is not done from a
10 KrishnaRajan
Figure1.8 Anexampleofdataintegrationinmaterialsengineeringmappingcorrela-
tionsbetweenmechanicalpropertiesoverawiderangeofmaterials:Fracturetough-
ness and modulus for metals, alloys, ceramic, glasses, polymers and metallic glasses.
ThecontoursshowthetoughnessG inkJ/m2.(FromAshbyandGreer,2006.)
c
purely statistical perspective but one where one carefully integrates a
physics-driven approach to data collection with data mining; it is validated
oranalyzedwiththeory-basedcomputationand/orexperiments.
The origins of the data can be either from experiment or computation
and the former, when organized in terms of combinatorial experiments,
can provide an opportunity to screen large amounts of data in a high-
throughput fashion (see Figure 1.9).
The materials informatics pathway to knowledge discovery, however,
is not a linear one but rather an iterative process that can provide new
information at each “information cycle” (Figure 1.10).
As described by Kitano (2002), in terms of biological research, a cycle
of research begins with the selection of contradictory issues of biological
significance and the creation of a model representing the phenomenon.
Models can be created either automatically or manually. The model
represents a computable set of assumptions and hypotheses that need to
be tested or supported experimentally. A similar analogy may be applied
to materials science in trying to explain unexpected or unusual materials
