Stanford University scientists have turned to
artificial intelligence (AI) and machine learning to help create a safer
lithium-ion battery.
Lithium-ion batteries that power smartphones, laptops
and other electronic devices have been known to explode and catch fire.
Researcher Austin Sendek, Stanford University Dept. of
Applied Physics. Credit: Stanford
Researcher Austin Sendek, Stanford University Dept. of
Applied Physics. Credit: Stanford
In August 2016, Samsung announced the recall of all
its newly issued Galaxy Note 7 smartphones after several of the devices
exploded on their users. The situation became so serious that in October, the
U.S. Department of Transportation along with the Federal Aviation
Administration issued an emergency order banning all Galaxy Note7 smartphone
devices from air transportation in the United States.
The cause of the problem, researchers suggest, is that
the liquid electrolytes that shuttle lithium ions back and forth between the
battery’s positive and negative electrodes can catch fire if the battery
overheats or is short-circuited.
Now, Stanford University researchers have identified
nearly two-dozen solid electrolytes that could someday replace these volatile
liquids based on techniques adapted from AI and machine learning.
“The main advantage of solid electrolytes is
stability,” says lead researcher Austin Sendek, a doctoral candidate in applied
physics. “Solids are far less likely to blow up or vaporize than organic
solvents. They’re also much more rigid and would make the battery structurally
stronger.”
Despite years of investigation, researchers haven’t
found an inexpensive solid material that performs as well as liquid
electrolytes at room temperature. But instead of continuing to test individual
compounds, Stanford researchers used AI to build predictive models from
existing experimental data.
“The number of known lithium-containing compounds is
in the tens of thousands, the vast majority of which are untested,” Sendek
says. “Some of them may be excellent conductors. We developed a computational
model that learns from the limited data we already have, and then allows us to
screen potential candidates from a massive database of materials about a
million times faster than current screening methods.”
Still, the team spent more than two years gathering
all known scientific data about solid compounds containing lithium. Their model
used several criteria to screen promising materials, including stability, cost,
abundance and their ability to conduct lithium ions and re-route electrons
through the battery’s circuit.
“We screened more than 12,000 lithium-containing
compounds and ended up with 21 promising solid electrolytes,” Sendek says. “It
only took a few minutes to do the screening. The vast majority of my time was
actually spent gathering and curating all the data.”