Aerospace & Defense Technology - October 2024 - 33

Tech Briefs
" In the early days of AI in materials
science, propelled by the White
House's Materials Genome Initiative
over a decade ago, research in this
field was largely curiosity-driven, " said
Ramprasad, a Professor in the School
of Materials Science and Engineering.
" Only in recent years have we begun to
see tangible, real-world success stories
in AI-driven accelerated polymer discovery.
These successes are now inspiring
significant transformations in the
industrial materials R&D landscape.
That's what makes this review so significant
and timely. "
AI Opportunities
Ramprasad's team has developed
groundbreaking algorithms that can
instantly predict polymer properties and
formulations before they are physically
created. The process begins by defining
application-specific target property or
performance criteria. Machine learning
(ML) models train on existing material-property
data to predict these desired
outcomes. Additionally, the team can
generate new polymers, whose properties
are forecasted with ML models.
The top candidates that meet the target
property criteria are then selected for
real-world validation through laboratory
synthesis and testing. The results from
these new experiments are integrated
with the original data, further refining
the predictive models in a continuous,
iterative process.
While AI can accelerate the discovery
of new polymers, it also presents unique
challenges. The accuracy of AI predictions
depends on the availability of
rich, diverse, extensive initial data sets,
making quality data paramount. Additionally,
designing algorithms capable of
generating chemically realistic and synthesizable
polymers is a complex task.
The real challenge begins after the
algorithms make their predictions: proving
that the designed materials can be
made in the lab and function as expected
and then demonstrating their scalability
beyond the lab for real-world use.
Ramprasad's group designs these materials,
while their fabrication, processing,
and testing are carried out by collaborators
at various institutions, including
Georgia Tech. Professor Ryan Lively from
the School of Chemical and Biomolecular
Engineering frequently collaborates
with Ramprasad's group and is a co-author
of the paper published in Nature
Reviews Materials.
" In our day-to-day research, we extensively
use the machine learning models
Rampi's team has developed, " Lively
said. " These tools accelerate our work
and allow us to rapidly explore new
ideas. This embodies the promise of ML
and AI because we can make model-guided
decisions before we commit time and
resources to explore the concepts in the
laboratory. "
Flow Measurement For
Critical Systems
That's what we do.
Hoffer Flow Controls is a proud supplier of flow measurement
solutions for the challenging applications of NASA and Private
Launch companies including Liquid Oxygen and Liquid Hydrogen for
Engine Testing, Nitrogen Gas measurement in Inflations Systems, and
many other flow applications.
" Specializing in flow measurement solutions
for the aerospace industry "
800-628-4584 www.hofferflow.com
Aerospace & Defense Technology, October 2024
ADT Hoffer Flow Controls Ad 0623.indd 1
mobilityengineeringtech.com
33
5/16/23 11:56 AM
http://info.hotims.com/86260-801 https://mobilityengineeringtech.com
Aerospace & Defense Technology - October 2024

Table of Contents for the Digital Edition of Aerospace & Defense Technology - October 2024
