HPCI magazine vol.4

Innovative Materials
Development with
Computer Science,
Data Science,and HPC
Materials Informatics (MI) is an attempt to improve the efficiency and sophistication ofmaterials development by incorporating data science methods. While the use of MI inmaterials development is making headway thanks to advances in machine learning, amajor obstacle that remains is a lack of data about the properties of the countless different
materials known to exist. To tackle this problem, a team at Sumitomo Chemical lead by
Shinya Nishino is using large-scale simulations to create a materials database suitable formachine learning. In this article, we look at this new materials development approach thatcombines computer science with data science and high performance computing.

Shinya Nishino

Team Leader
Digital & Data Science Innovation Department,
Sumitomo Chemical Co., Ltd.

Materials Informatics (MI):Transforming How Materials
are Developed
Until recently, the development of materials
for various industrial uses has mostly reliedon the wisdom and intuition of experienced
researchers and engineers. But, as customers'
needs have grown more diverse and R&D
time has been curtailed, it is becoming moreand more difficult to address these needs withtraditional approaches.
Enter Materials Informatics (MI), which brings
AI and data science to materials development
to drastically shorten development times anddiscover innovative materials with novelproperties. Shinya Nishino at materials maker
Sumitomo Chemical has striven to make01

Explanation 1

How MI is being used in Materials Development
Materials Informatics can improve common development tasks:High Throughput Screening

Inverse Problem Approaches

MI can quickly find the most suitable from agroup of candidate materials.

MI can start with the desired properties andwork backwards to find the materials that havethose properties.

Case 1: Using machine learning to screen 1.6 billion
molecules to find OLED (Organic Light Emitting Diode)materials with 22% external quantum efficiency.
R Gómez-Bombarelli, et al., Nature Materials 15, 1120-1127 (2016)

Case 2: Using Bayesian inference to solve an inverse
problem and successfully build a system that suggests
molecular structures with the targeted properties.
H. Ikebata, et al., J. Computer Aided Molecular Design (2017) 31:379-391

Retrosynthesis

Efficient Experiment DesignMI can learn from existing data to predict themost efficient pathways (processes) tosynthesize the desired molecules.

MI can help plan the most efficient experimental
approach when investigating a particularproperty of materials.

Case 3: Learning documented synthetic pathways to predict
new synthetic pathways with greater than 50% accuracy.

Case 4: Using Bayesian optimization on existing data tominimize the number of physical tests in discovering optimal
structures for thermoelectric materials.

M. H.S. Segler, M. P. Waller, “Modelling Chemical Reasoning toPredict and Invent Reactions”, Chem. Eur. J. 2017, 23, 6118,
Copyright Wiley-VCH GmbHvol.4 - Innovative Materials Development with Computer Science, Data Science, and HPC

practical use of MI in the development ofmaterials.
“When a new smartphone model comes
out, for example, materials manufacturers
have to provide new materials for abetter-looking display or a longer battery
life. Responding to these requirementswith conventional approaches to materials
development is time consuming, and finding
new materials is becoming ever moredifficult. Materials manufacturers arebecoming increasingly aware of the needfor a different approach to materials
development, and MI is one of the answers.”

S. Ju, et al., Phys. Rev. X 7, 021024 (2017)02

process conditions you want for a particulardevelopment target, you may find that
there are only 100 data sets at most. This
lack of materials data is the biggest hurdle
to implementing Materials Informatics.”

The Bottleneck is a Lackof Data on Materials
In Materials Informatics, a prediction modelis created through machine learning. Thatmodel is then used to predict the synthetic
pathways and other properties of candidatematerials. What is important here is thatthe prediction model can replace theintuition and experience of researchersand engineers, and that a database ofmaterial properties can be used to create
the model. According to Nishino, however,
the availability of such materials databases
lags far behind those for other fields, suchas drug discovery.
“Big data approaches are currently focusedon fields with data that is more readily
available. Unfortunately, there is very little
data in the field of materials. If you try tofind materials data with the features andvol.4 - Innovative Materials Development with Computer Science, Data Science, and HPC

To address this problem, Nishino had theidea of running large-scale computer
simulations to automatically create a
database of materials and their predicted
properties. First, they run computer
simulations to understand the mechanism
behind a particular property of a material.
Next, they map that property to a numericvalue known as a “descriptor”. Those
descriptors can then be used to predict theproperties of other materials at a lower
computational cost. And then machinelearning is applied to the materials data
generated from those descriptors.

“If large-scale simulations on highperformance computers can be used togenerate materials data that was previously
unavailable, we can get a competitive
advantage in development. Combining
Materials Informatics with that data canmake it much more valuable. I expectedthat a combination of computer science,
data science, and HPC could revolutionize
materials development.”

A Combination of
Large-scale Computer
Simulations and Machine
Learning
Nishino applied this “materials data fromsimulations” approach to research onorganic light emitting diode (OLED)
materials, which are commonly used toilluminate the displays of modern smartphones and televisions. An OLED material’s
ability to emit light is a property known as
its “luminescence”. The luminescence isdetermined by how certain electrons moveor “hop” within the molecular structure of agiven material, an important phenomenon
known as “charge transfer”. To better
understand how charge transfer occurs invarious OLED materials, Nishino ran
large-scale simulations of quantum wave
packet dynamics in collaboration with03

Takeo Hoshi, an Associate Professor at the Tottori University
Graduate School of Engineering. Because these calculations
require advanced algorithms and powerful computing resources,
they used the Oakforest-PACS supercomputer at the Japan
Center for Advanced High Performance Computing, which was
the fastest computer available in 2019.
These were “numerical simulations”, mathematical models ofphysical systems that allowed Nishino to analyze how electricalcharges move around inside OLED materials. He discoveredthat the electrical charge transfers in an OLED material can bepredicted using a “physical quantity” called the “inverseparticipation ratio” (IPR) as a molecular descriptor that indicatesthe material's properties. Using this IPR descriptor to sort thematerials according to their charge transfer property allowed theresearch team to generate a materials database suitable for use
as input for machine learning.
“It’s often better to use a particular property of a material as themachine learning input, rather than just the material's molecularstructure. This is because models based on an underlying
principle are generally more versatile. At this point, it's important
to know what kind of descriptors to put into the model, which ishard to know with pure data science alone. It requires a knowledgeof physics and chemistry, or experience working with the material.
With that kind of knowledge and experience, it's easier to recognizethat a particular section can be reduced to a feature (a physicalquantity suitable for machine learning), and that it can bequantified.”vol.4 - Innovative Materials Development with Computer Science, Data Science, and HPC

Explanation 2

Large-scale quantum simulations of chargetransfers in OLED materials

The so-called “hopping” transfer of electrical charge within a molecule is the crucial
physical phenomenon that determines the luminescent properties of a given OLEDmaterial. In this study, Nishino used numerical simulations of quantum wave packet
dynamics to investigate how electrical charge propagates through the molecules ofmore than a dozen OLED materials with different combinations of host molecules andguest molecules, known as “host-guest complexes”. These numerical simulations wererun by Tottori University Graduate School of Engineering Associate Professor Takeo
Hoshi, an expert in large-scale high-performance computing simulations.
The figure below illustrates a charge transfer that occurs in OLED molecules asrevealed by these numerical simulations. The simulations showed how the wavefunctionsof two complexes overlap when a charge jumps from one complex to the other. This
research demonstrated that the charge transfer property for a given OLED material canbe predicted by a physical quantity called the “inverse participation ratio” (IPR).
Calculating this IPR descriptor is much less computationally expensive than running
quantum simulations, thereby lightening the computational load for predicting a material'sproperties. Nishino says his familiarity and experience with condensed matter physics
theory was very helpful in discovering the IPR's usefulness a descriptor.
This series of images taken from the wave packet dynamics simulations shows how thewave packets move. The blue regions show the “hole” wave packets, the location of thecharge. We can see that the charge jumps from place to place.

04

From Materials Informatics
to the Cyber-Physical
Systems Loop
Nishino says he wants to use HPCI
resources like Fugaku to further apply thisnew “computer science meets data
science meets HPC” approach to materials
development.
“It's increasingly vital to create data thatothers can't get, and to understandmechanisms that haven't been understood
thus far. So, I think advanced simulations

vol.4 - Innovative Materials Development with Computer Science, Data Science, and HPC

Verifying the categories of materials producedwith the IPR descriptor

The figures below show the results of tests to verify whether using the IPR as a descriptor accurately categorized theOLED materials simulated. The IPR is a multi-dimensional parameter, so the “principal component analysis” technique
was used to reduce the number of dimensions when verifying the results. The left figure shows changes in the molecular
host-guest pair complexes. The right figure shows changes in the concentration of these complexes. Both results show
distinct differences for materials with different host-guest complex compositions. This suggests that a machine learningmodel that uses the IPR descriptor data to categorize materials should be effective in predicting the luminescence
properties of OLED materials.

Host-guest
molecular complexes

Host-guest
molecular complexes
Component #3“I used to do research in theoreticalcondensed matter physics, which is how I
determined the IPR could be used as adescriptor. The team members on thisproject include specialists in materials
development, computer science, and datascience. Plus, we have support from RIST,
which is a leading organization for HPCIwork. I think that diversity of expertise
helped lead to this achievement.”

Explanation 3

Component #2

The IPR descriptor that Nishino found in
this research is a physical quantity that iscovered in the basic theory of condensed
matter physics. But he says it has rarely
been used in the field of materials science.

Component #1

using HPC and data generation via thosesimulations will become even more
important. What I want to work on in thefuture is a way to create machine learningmodels for the kinds of materials that arerelatively easy to simulate, and then using
those models even in areas where it'sdifficult to acquire data. This is called'transfer learning'. It's often used in image

Component #1

analysis. But I think it could also be of use inmaterials development.”
Nishino hopes to create a more advanced
and data-driven approach to materials
development, one that combines theory,experimentation, simulations, and data
science.

05

“At this stage, we've combined theory,computer science, and data science. But
our goal is to add experimentation to this,generate new theories and new models
based on the findings of those experiments,
and then generate new data throughsimulations based on those models. The
goal for the next iteration of Materials
Informatics is realizing a materials
development cycle called the 'cyber-physicalsystems loop'. And I think my role is inbuilding a system that can sustainably
create value by developing human resources
who can master MI and the cyber-physicalsystems loop.”

About theResearcher
Shinya Nishino says he studied the basic
theory of physical properties at university,and worked in materials research which
combined that basic theory with computer
simulations as a graduate student and postdoctoral researcher. At that time, he andTakeo Hoshi at Tottori University developed
the software that was used for these numericalsimulations.
Nishino first became interested in Materials
Informatics as a postdoctoral researcher.
When running numerical simulations ofmaterials for lithium-ion batteries at therequest of a Japanese auto maker, he saw
how Materials Informatics was being usedby competitors abroad to examine candidate
materials, and sensed the great potential ofthis field.
Nishino's comprehensive experience withmaterials theory, large-scale computer
simulations, and actual materials
development in practice is likely the key tohis success as a pacesetter in Materials
Informatics.

Associated Research Projects• “Organic Semiconductor Material Design using Large-Scale Quantum Simulations and Machine Learning” (hp190066)• “Design of Organic Semiconductor Materials by Machine Learning Model built from Big Data Generated by Large Scale Quantum
Chemical Calculations” (hp200049)

vol.4 - Innovative Materials Development with Computer Science, Data Science, and HPC

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