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In
a research paper published in science journal Nature on
Wednesday, the Alphabet-owned AI firm said almost 400,000 of its
hypothetical material designs could soon be produced in lab
conditions.
Potential applications for the research include the production
of better-performing batteries, solar panels and computer chips.
The discovery and synthesis of new materials can be a costly and
time-consuming process. For example, it took around two decades
of research before lithium-ion batteries – today used to power
everything from phones and laptops to electric vehicles – were
made commercially available.
“We're hoping that big improvements in experimentation,
autonomous synthesis, and machine learning models will
significantly shorten that 10 to 20-year timeline to something
that's much more manageable,” said Ekin Dogus Cubuk, a research
scientist at DeepMind.
DeepMind’s AI was trained on data from the Materials Project, an
international research group founded at the Lawrence Berkeley
National Laboratory in 2011, made up of existing research of
around 50,000 already-known materials.
The company said it would now share its data with the research
community, in the hopes of accelerating further breakthroughs in
material discovery.
"Industry tends to be a little risk-averse when it comes to cost
increases, and new materials typically take a bit of time before
they become cost-effective," said Kristin Persson, director of
the Materials Project.
"If we can shrink that even a bit more, it would be considered a
real breakthrough."
Having used AI to predict the stability of these new materials,
DeepMind said it would now turn its focus to predicting how
easily they can be synthesized in the lab.
(Reporting by Martin Coulter; Editing by Jan Harvey)
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