HPCI magazine vol.5

Computational andData Science for
Next-generation
Batteries with Safety
and High Performance
To address the threat of climate change, Japan and other countries have embraced the goal of
becoming carbon neutral and reducing their greenhouse gas emissions to zero by 2050.
High-performance rechargeable batteries are expected to provide a decisive advantage inachieving this goal. Solid-state batteries in particular are expected to be widely used in electricvehicles, and the race to develop them is taking off around the world. To use the powers ofcomputer science and data science to help develop solid-state batteries, Yoshitaka Tateyama's
research group has been making steady progress in the quest to understand the underlying
mechanisms of suitable materials for these new batteries. Their “Fugaku Battery & Fuel Cell”*1
project falls under the “Program for Promoting Research on the Supercomputer Fugaku” rubric.

Yoshitaka Tateyama

Group Leader
Interface Computational Science Group,Center for Green Research on Energy and Environmental Materials,
National Institute for Materials Science*1 Research project name:“Computational and Data ScienceS t u d y fo r E T Revo l u t i o n by
Development of Next-GenerationBattery and Fuel Cell” (Principal
Investigator: Yoshitaka Tateyama)

Solid-state Batteries to Make
Electric Vehicles Mainstream
Electric vehicles themselves do not emit
carbon dioxide while running. So, if the electricity used to charge them is obtained fromrenewable energy sources, the total carbon
dioxide emissions should be drastically lessthan with gasoline-powered vehicles. Oneof the key factors in the adoption of electricvehicles is developing safe, efficient batteries.
At present, electric vehicles are equippedwith lithium-ion batteries similar to thoseused in smartphones and other portabledevices. But the liquid electrolytes used inthese lithium-ion batteries are flammable andrequire strict safety measures. To mitigate
these problems, both academic and industrial
researchers are working on battery designs
01with solid electrolytes instead of the liquidones.
“Solid-state batteries are not only safer;they can also be more efficient. With
energy and power densities greater thanthose of conventional lithium-ion batteries,
solid-state batteries are expected toextend the cruising range of electricvehicles, improve acceleration, and shorten
recharging times,” says Tateyama.
But developing solid-state batteries is notan easy task. The biggest challenge is theneed for a solid electrolyte material that
conducts lithium ions well. In bothconventional and solid-state batteries,
lithium ions travel between the positive
and negative electrodes during charging
and discharging (Fig.1). The organic liquid
electrolyte in a conventional battery allows
lithium ions to flow through freely. But thesolid electrolyte particles in a solid-state

vol.5 - Computational and Data Science for Next-generation Batteries with Safety and High Performancebattery impede the lithium ions' ability topass through, limiting the battery's
charge-discharge performance.
Researchers are therefore investigating a
range of candidate materials to find a solid
electrolyte that will allow lithium ions topass through faster. Physically testing theimmense number of candidate materials,
however, would require enormous time andeffort. That's where Tateyama and his teamcome in. They are using supercomputers
like Fugaku to search for solid electrolytematerial candidates.

Using Machine Learning to
Search for Promising Solid
ElectrolytesTo efficiently search for materials that canpass lithium ions very quickly, Randy
Jalem of Tateyama's group devised a
new approach to the calculations (Fig. 2).
First, based on structures found in a
database of known materials, tens of
thousands of hitherto unknown materials
are generated on a computer. Next, using
clues such as the shape of the pathway forlithium ions and the types of elements
around the pathway, promising candidates
are selected from among the many materials
generated. Then, machine learning withFig. 1

Lithium-ion Battery Designs

In solid-state batteries, the conventional organic
liquid electrolyte (a) between the positive andnegative electrodes is replaced with a solid electrolyte material (b). Both designs operate the same way,with lithium ions transfering back and forth between
the positive and negative electrodes when charging
or discharging the battery.

(a) Conventional Li-ion Battery
chargee-e-discharge
Al current
collector

Cu current
collector

negative
electrodeorganic
electrolyte/ separator

positive
electrode(b) Solid-state Battery
chargee-e-discharge
Al current
collector

Cu current
collectornegativeelectrodesolid electrolyte

positiveelectrode02

Fig. 2The computational process fordiscovering solid electrolyte
candidates and its inventor,Randy Jalem.

(1) Generate tens of thousands of solid electrolytes.

Jalem says, “With this approach, I found
two promising candidates. Those two will
be synthesized in a lab and tested for theirperformance.” The results show thatlarge-scale computations can speed up
research by selecting candidate materials
logically.

The Steps to Discovery:(1) Rearrange the atoms of known crystal structures togenerate tens of thousands of solid electrolyte candidates.
(2) Narrow down the candidates based their lithium ion
pathways and the types of elements around the pathways.
(3) Use Bayesian optimization to select the remaining
candidates with higher lithium ion conductivity.
The remaining candidate materials discovered through thisprocess were actually synthesized in the laboratories of theTokyo Institute of Technology.

Bayesian optimization is used to select thematerials that should allow the lithium ionsto pass through the fastest.

(2) Identify candidate structures with suitable
lithium-ion pathways.

Randy JalemSenior Researcher
Interface Computational Science Group,Center for Green Research on Energy andEnvironmental Materials

Jalem enthuses, “When we were using theK computer, the materials searchcalculations took weeks, because we haveto iterate through loop statements somany times. With Fugaku's huge numberof high-performance nodes, we can run
the calculations much faster. So, we could
run a search with many more candidate
materials.”(3) Select candidate structures withhigher lithium-ion conductivity.
Two promising candidates found

Synthesize and evaluate

vol.5 - Computational and Data Science for Next-generation Batteries with Safety and High Performance03

Fig. 3Heterogeneous Interface
CALYPSO Method

First, in the interface section between the twoAtoms & ions
Solid substance A

different types of solids (A and B), various arrangements
of atoms and ions from these two solids are used toGenerating manydifferent interface
structuresgenerate tens of thousands of different interface
structures. While the energy of each candidate
interface structure is being calculated, the positions

Sampling thelow-energy(probable)interfacestructures

Interfaceof its atoms and ions are optimized. From these, thestructures with the lower energies, meaning moreprobable structures, are selected.
Solid substance B

Massive Searches forInterfaces to Maximize
Battery Performance
Another major issue in the development ofsolid-state batteries is controlling theinterface between the solid electrolyte andeach electrode material. Researching anddeveloping the coating layers is particularlyimportant. “Actually, solid-state battery
performance tends to degrade over timewith repeated charging and discharging.
We know that a thin coating of a different
material applied to the surface of thevol.5 - Computational and Data Science for Next-generation Batteries with Safety and High Performanceelectrode material can effectively prevent
this degradation, but we don't exactly
know how that works. So, the strategy has
not been quite established which material
is best to use or how thick that coating
should be,” explains Tateyama. To make thebest use of lab testing resources, he isusing computer science to better understand
how lithium ions and electrons travel
through the materials that comprise theelectrodes, the coating layer, and the solidelectrolyte.
Tateyama's group developed an AI-based

CALYPSO (Crystal structure AnaLYsis byParticle Swarm Optimization, Fig. 3) method
to predict the structures at the “heterogeneous interface” where the atoms andions of two different materials adjoin instructures with varying arrangements.
The CALYPSO method generates tens of
thousands of these atomic interface
structures, and efficiently calculates theenergy of each to predict which structures
are more stable and therefore more likely
to occur. Using this method, Tateyama
was able to find stable structures for boththe electrode-to-coating interface and the04Fig. 4

coating-to-solid electrolyte interface (Fig. 4).
This was Tateyama and his team's gamechanging work with computer simulations
to automate searches for stable interface
structures. The computing power of Fugaku
enabled them to search an enormous
number of these structures. The team is
now studying the behavior of the lithium
ions and electrons in the structures they
discovered to better understand how thecoating layer suppresses degradation.

guidelines for interface design by showing
what's actually happening at the interface.
To calculate the energy of the interface
structure, we use a precise method called“first-principles calculations”,*2 which requires
a huge amount of calculating for even asingle structure. But with Fugaku, we cancompute the energies of many structures atonce in parallel, so our research can movequickly,” says Tateyama. The combination“Controlling the interface as well as thecoating is key to developing solid-statebatteries. So, we'd like to help formulate*2 First-principles calculation: A calculation that follows thefundamental laws of physics without the use of empirical parameters. In this case, it refers to calculating a property such as theenergy of a material according to quantum mechanics.

Coating Layer Research
The coating layer is sandwiched between an electrode material on one side (in thisexample, the positive electrode material), and the solid electrolyte material on theother. The particles comprising the electrode material and the solid electrolyte
Solid electrolyte(Li³PS⁴)

Coating layer(LiNbO³)

Stable structure found withthe heterogeneous-interface
CALYPSO method

material are a few micrometers in size, but the coating layer is much thinner, only
a few nanometers. The heterogeneous interface CALYPSO method was used tofind possible interface materials with a stable structure on both the electrode-facingside and the solid electrolyte-facing side. The conductivity of the structures in thosecandidate interface materials was then studied to determine how easily lithium ions

+ Electrodematerial(LiCoO²)and electrons could pass through them. The right side of the figure shows anexample of the calculated interface structures between the solid electrolyte material
and the coating layer. In practice, the interfaces between many different materials
were studied.

vol.5 - Computational and Data Science for Next-generation Batteries with Safety and High Performance05

of laboratory testing and Fugaku's calculations will greatly accelerate the researchand development of solid-state batteries.
Computer science and data science will help
createhigh-performancesolid-statebatteries, which in turn will help us achievecarbon neutrality as electric vehicles
become the norm.

About theResearcherYoshitaka Tateyama decided to become ascientist in elementary school upon reading
the biography of physicist Lev Landau andbeing captivated by his forthright attitudetoward science. Tateyama says his mottoconcerning work is “to do groundbreakingresearch”. To that end, he values actively
communicating with people from all walks oflife. There is a lot to be learned fromconversations, and sometimes the seeds offuture research can be found throughinteractions with experimenters. A nicedrink or an enjoyable meal can grease thewheels of communication. But for now,
the COVID-19 pandemic has put suchopportunities on hold.

Associated Research Projects• “Computational and Data Science Study for ET Revolution by Development of Next-Generation Battery and Fuel Cell”(hp200131/hp210173)
Principal Investigator: Yoshitaka Tateyama, National Institute for Materials Science

vol.5 - Computational and Data Science for Next-generation Batteries with Safety and High Performance

06

HPCI magazine FUGAKU HYAKKEI vol.5

Registered Institution for Facilities Use Promotion /The Representative for HPCI Operation

Research Organization
for Information Science and Technology
1-5-2, Minatojimaminami-machi, Chuo-ku, Kobe, Hyogo, 650-0047 JAPAN
Tel: +81-78-599-9511

HPCI Portal Site

HPCI PR site

https://www.hpci-office.jp/folders/english

https://fugaku100kei.jp/e/Issued: October 2021
© 2022 Research Organization for Information Science and Technology