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Machine learning boosts
electrolyte search
The search for the optimum molecules
for use as electrolytes in lithium-ion batteries involves an examination of billions of
potential candidates. The challenge, according to scientists at the U.S. Department of
Energy's Argonne National Laboratory, lies
in the tradeoff between molecular modeling
a ccu ra c y a nd comput at iona l cost .
Fortunately, artificial intelligence and machine learning may be able to help.
One of the scientists' tools is a computationally intensive model called G4MP2. Using
this tool, the scientists accurately modeled
tens of thousands of small organic molecules,
as described in a recent paper. "Energies for
the ˜133,000 molecules in the GDB-9 database, containing organic molecules having
nine or less atoms of carbon, nitrogen, oxygen, and fluorine as well as hydrogen atoms,
have been calculated at the G4MP2 level of
theory," the authors write.1 (GDB-9 refers to
a database available at the Wolfram Data
Repository of molecular quantum calculations describing geometric, energetic, electronic, and thermodynamic properties.)
However, those 133,000 molecules represented only a small subset of 166 billion
total molecules, including large ones, the
scientists wanted to evaluate as potentially
suitable for use as electrolytes. Applying the
G4MP2 model to that huge number of molecules would be a computationally impossible
task, even for the BEBOP supercomputing
cluster at Argonne's Laboratory Computing
Resource Center, which the scientists used
in their research.
That's where machine learning comes in.
The authors explain that the G4MP2 energies of the GDB-9 molecules will be useful
in future investigations of the application of
machine learning to quantum chemical data.
A second paper describes the details. The
researchers applied a less computationally
intensive quantum-mechanical modeling
framework based on density functional theory,
which is less accurate than G4MP2. But by
using the G4MP2 results, they could train the

density-functional-theory model to improve its
accuracy while keeping compute costs down.
"Our resulting models learn the difference
between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005
eV (mean absolute error) for molecules with
less than nine heavy atoms (training set of
117,232 entries, test set 13,026) and 0.012 eV
for a small set of 66 molecules with between
10 and 14 heavy atoms," the authors of the
second paper write.2
"When it comes to determining how these
molecules work, there are big tradeoffs between accuracy and the time it takes to compute a result," said Ian Foster, Argonne Data
Science and Learning division director and
an author of one of the papers, as quoted at
Newswise. 3 "We believe that machine learning represents a way to get a molecular picture that is nearly as precise at a fraction of
the computational cost."
"The machine-learning algorithm gives us
a way to look at the relationship between the
atoms in a large molecule and their neighbors,
to see how they bond and interact, and look
for similarities between those molecules and
others we know quite well," added Argonne
computational scientist Logan Ward, an author of one of the studies. "This will help us to
make predictions about the energies of these
larger molecules or the differences between
the low- and high-accuracy calculations."
Machine learning has demonstrated its
usefulness in application areas ranging from
banking to medicine. It's applicability to the
search for stable, safe electrolytes for lithiumion batteries represents yet another.
REFERENCES

1. Narayanan, Badri, et al., "Accurate quantum
chemical energies for 133,000 organic molecules,"
Chemical Science, June 27, 2019.
2. Ward, Logan, et al., "Machine learning prediction
of accurate atomization energies of organic molecules
from low-fidelity quantum chemical calculations,"
MRS Communications, September 2019.
3. "Building a better battery with machine learning,"
Newswise, Nov. 26, 2019.

2.11 BILLION
Semiconductor equipment
billings in October

3.9%
Increase in billings
from October 2018
Source: SEMI

6.2%
Increase in October
North American PCB
shipments year-to-year

7%
Year-to-date October North
American PCB order growth
Source: IPC

1.5%
Predicted worldwide
smartphone market
growth in 2020

190 MILLION
5G smartphone shipments
expected in 2020
Source: IDC

EVALUATION ENGINEERING JANUARY 2020

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Evaluation Engineering

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