Tech Briefs Magazine - May 2024 - 31

Then the extra cobalt is there to facilitate
the formation of the C-C bonds that link
up the carbon atoms, " she explained.
The ease of catalyst recycling, commercial
availability of the catalysts, and relatively
mild reaction conditions for the second
reaction all contribute to a favorable
assessment of the energy and other costs
associated with the process, the researchers
said.
" For practical applications, both are
really important - the CO₂ footprint
analysis and the recyclability of the catalyst, "
said Chen. " Our technical results
and these other analyses show that this
tandem strategy opens a door for decarbonizing
CO₂ into valuable solid carbon
products while producing renewable H₂. "
If these processes are driven by renewable
energy, the results would be truly carbon-negative,
opening new opportunities
for CO₂ mitigation.
For more information, contact Karen
McNulty Walsh at 631-344-8350.
AI Identifies New Materials for Carbon Capture
A suitable material for effective carbon capture at low cost has yet to be found. One
candidate is metal-organic frameworks.
Argonne National Laboratory, Lemont, IL
C
arbon capture is a critical technology in
reducing greenhouse gas emissions
from power plants and other industrial facilities.
But a suitable material for effective
carbon capture at low cost has yet to be
found. One candidate is metal-organic
frameworks, or MOFs. This porous material
can selectively absorb carbon dioxide.
MOFs have three kinds of building
blocks in their molecules - inorganic
nodes, organic nodes, and organic linkers.
These can be arranged in different relative
positions and configurations. As a result,
there are countless potential MOF configurations
for scientists to design and test.
To speed up the discovery process, researchers
from the U.S. Department of
Energy's (DOE) Argonne National Laboratory
are following several pathways.
One is generative artificial intelligence
(AI) to dream up previously unknown
building block candidates. Another is a
form of AI called machine learning. A
third pathway is high-throughput screening
of candidate materials. And the last
is theory-based simulations using a method
called molecular dynamics.
By exploring the MOF design space with
generative AI, the team was able to quickly
assemble, building block by building block,
over 120,000 new MOF candidates within
30 minutes. They ran these calculations on
the Polaris supercomputer at the Argonne
Leadership Computing Facility (ALCF).
They then turned to the Delta supercomputer
at UIUC to carry out time-intensive
molecular dynamics simulations,
using only the most promising candidates.
The goal is to screen them for stability,
chemical properties, and capacity
for carbon capture.
" People have been thinking about MOFs
for at least two decades, " said Argonne's
Eliu Huerta, Study Lead. " The traditional
methods have typically involved experiTech
Briefs, May 2024
Scientific visualization of the AI-guided assembly of a novel metal organic framework with high
carbon dioxide adsorption capacity and synthesizable linkers. Building blocks, predicted by generative
AI, are shown on the left, while the final AI-predicted structure is shown on the right. (Image:
Xiaoli Yan/University of Illinois Chicago and the ALCF Visualization & Data Analytics Team)
mental synthesis and computational modeling
with molecular dynamics simulations.
But trying to survey the vast MOF landscape
in this way is just impractical. "
" We wanted to add new flavors to the
MOFs that we were designing, " added
Huerta. " We needed new ingredients for
the AI recipe. "
The team's algorithm can make improvements
to MOFs for carbon capture by
learning chemistry from biophysics, physiology,
and physical chemistry experimental
datasets that have not been considered for
MOF design before.
To Huerta, looking beyond traditional
approaches holds the promise of a transformative
MOF material - one that could
be good at carbon capture, cost-effective
and easy to produce.
" We are now connecting generative
AI, high-throughput screening, molecular
dynamics, and Monte Carlo simulawww.techbriefs.com
tions
into a standalone workflow, " Huerta
said.
" This workflow incorporates
online learning using past experimental
and computational research to accelerate
and improve the precision of AI to
create new MOFs. "
The atom-by-atom approach to MOF design
enabled by AI will allow scientists to
have what Argonne Senior Scientist and
Data Science and Learning Division Director
Ian Foster called a " wider lens " on these
kinds of porous structures.
" Work is being done so that, for the new
AI-assembled MOFs that are being predicted,
we incorporate insights from autonomous
labs to experimentally validate their
ability to be synthesized and capacity to
capture carbon, " Foster said. " With the
model fine-tuned, our predictions are just
going to get better and better. "
For more information, contact Julie
Parente at jparente@anl.gov.
31

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Tech Briefs Magazine - May 2024

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