BOSTON, Aug. 5, 2020 /PRNewswire/ -- Nearly every sector
is proposing the use of artificial intelligence. Materials science
R&D is relatively late to this trend, and there are many
industry-specific hurdles, but the opportunities are beginning to
be realized and the potential impact is significant.
Materials informatics is the use of data-centric approaches for
materials science discovery and development. This is principally
enabled by improved data infrastructures and machine learning
solutions; this is set to be a paradigm shift in the way
researchers conduct R&D projects and a discussion on why the
adoption is now can be seen in a previous article. At this key
moment of initial commercial adoption, IDTechEx has released the
most comprehensive technical market report on the topic, "Materials
Informatics 2020-2030".
Materials informatics can be used at every stage of an
experimental process as outlined in the infographic below. There
are multiple potential advantages including identifying new species
or relationships, extracting value from existing data, and
generating use-case IP on existing compounds, but in most cases it
is all about accelerating the time to market and providing a
competitive advantage.
Quantifying this accelerated time to market is difficult but
essential for external companies to demonstrate and justify any
investment. Many claim extensive examples of reducing millions of
candidates and/or thousands of experiments to more manageable
hundreds, or even tens, of solutions or iterations.
IDTechEx has classified the projects undertaken into 6 main
categories outlined in detail within the report. Previous articles
have shown how this has been used in multiple applications
already.
A key concept is the idea of an "inverse design". In simple
terms, this can involve training a model that allows properties to
be input and formulations, compositions, process parameters or more
to be proposed. The properties do not have to just be physical but
could also be cost, toxicity, geographic availability or more. The
technology is applicable to anyone that designs materials or
designs with materials, an aim is to have this inverse design fully
integrated with initial product design. This has been most
effectively shown by the collaboration between Citrine Informatics
and Siemens. It was stated that they want designers to view
material as one of their "degrees of freedom" and allow materials
companies to become "partners not vendors".
For clarity, materials informatics is not to be confused with
computational simulation (e.g. DFT calculations). This material
modeling has seen major progressions over the past few decades (led
by the likes of BIOVIA and Schrödinger) and with the continual
improvement in computing power this will only increase. The
announcement between JSR Corporation and QSimulate is notable
recent evidence towards this. The data can be used in the same way
as input data from any physical experimentation. In fact, a common
approach of MI is used in reducing the number of costly and
time-consuming simulations, facilitating these research projects,
and drawing novel relationships.
The main problem is the limitations of the materials dataset.
This is not like recognizing objects in autonomous vehicles or
sophisticated internet search engines, materials science brings
numerous specific problems. The data is typically sparse,
high-dimensional, biased, and noisy which means that understanding
the uncertainty in the proposed output is essential; projecting out
into the "unknown" is very challenging given the clustered, complex
data.
There are many approaches to dealing with small datasets, this
could involve generating one through high-throughput
experimentation, leveraging external data repositories and most
importantly integrating domain knowledge.
Generating and leveraging data repositories is a core theme of
materials informatics. There are a wide number of very bespoke or
more general repositories collecting published structures,
properties, and other data. These are run by public or private
organizations and, although may have limitations (such as unknown
confidence in the data and biased by only having "positive"
published results), they can be an unparalleled source for training
models or screening for candidates. Not to mention large datasets
opens the opportunity for utilizing more sophisticated deep
learning methods.
Accelerating the time from materials design to market is
essential. The material development cycle is normally far slower
than many end-user products and in certain sectors this development
and qualification can be the bottleneck. Materials informatics can
change that. For more information on this market see the IDTechEx
report, "Materials Informatics 2020-2030",
www.IDTechEx.com/MaterialsInformatics.
IDTechEx also covers many of the relevant application areas
highlighted throughout the report, for deeper-dive in each of these
markets including additive manufacturing, graphene & other
nanomaterials, energy storage, organic electronics, wearable
electronics, sensors and more visit www.IDTechEx.com/research.
IDTechEx guides your strategic business decisions through its
Research, Consultancy and Event products, helping you profit from
emerging technologies. For more information on IDTechEx Research
and Consultancy, contact research@IDTechEx.com or visit
www.IDTechEx.com.
Media Contact:
Natalie
Moreton
Digital Marketing Manager
press@IDTechEx.com
+44(0)1223 812300
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