Making new materials using AI

Date:
October 15, 2020
Source:
Pohang University of Science & Technology (POSTECH)
Summary:
Researchers demonstrate a novel physical phenomenon by controlling
variations of the atomic structure.



FULL STORY ========================================================================== There is an old saying, "If rubber is the material that opened the way
to the ground, aluminum is the one that opened the way to the sky." New materials were always discovered at each turning point that changed human history. Materials used in memory devices are also drastically evolving
with the emergence of new materials such as doped silicon materials,
resistance changing materials, and materials that spontaneously magnetize
and polarize. How are these new materials made? A research team from
POSTECH has revealed the mechanism behind making materials used in new
memory devices by using artificial intelligence.


==========================================================================
The research team led by Professor Si-Young Choi of Department of
Materials Science and Engineering and the team led by Professor Daesu
Lee of the Department of Physics at POSTECH have together succeeded in synthesizing a novel substance that produces electricity by causing polarization (a phenomenon in which the position of negative and
positive charges is separated from the negative and positive charges
within the crystal) at room temperature and confirmed its variation in
the crystal structure by applying deep neural network analysis. This
paper was published in the recent issue of Nature Communications.

The atomic structures of perovskite oxides are often distorted and
their properties are determined by the oxygen octahedral rotation (OOR) accordingly.

In fact, there are only a few stable OOR patterns present at equilibrium
and this inevitably limits the properties and functions of perovskite
oxides.

The joint research team focused on a perovskite oxide called CaTiO3 which remains nonpolar (or paraelectric) even at the absolute temperature of 0K.

Based on the ab-initio calculations, however, the team found that a unique
OOR pattern that does not naturally exist would be able to facilitate
the ferroelectricity, a powerful polarization at room temperature.

In this light, the research team succeeded in synthesizing a novel
material (heteroepitaxial CaTiO3) that possesses the ferroelectricity
by applying interface engineering that controls the atomic structures
at the interface and accordingly its physical property.

In addition, deep neural network analysis was applied to examine the
fine OOR and the variation of a few decades of picometer in the atomic structures, and various atomic structures were simulated and data were
utilized for AI analysis to identify artificially controlled OOR patterns.

"We have confirmed that we can create new physical phenomena that
do not naturally occur by obtaining the unique OOR pattern through
controlling the variation in its atomic structure," remarked Professor
Daesu Lee. "It is especially significant to see that the results of the convergent research of physics and new materials engineering enable calculations for material design, synthesis of novel materials, and
analysis to understand new phenomena." Professor Si-Young Choi explained,
"By applying the deep machine learning to materials research, we have successfully identified atomic-scale variations on tens of picometers
that are difficult to identify with the human eye." He added, "It could
be an advanced approach for materials analysis that can help to understand
the mechanism for creating new materials with unique physical phenomena."

========================================================================== Story Source: Materials provided by Pohang_University_of_Science_&_Technology_(POSTECH).

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jeong Rae Kim, Jinhyuk Jang, Kyoung-June Go, Se Young Park,
Chang Jae
Roh, John Bonini, Jinkwon Kim, Han Gyeol Lee, Karin M. Rabe,
Jong Seok Lee, Si-Young Choi, Tae Won Noh, Daesu Lee. Stabilizing
hidden room- temperature ferroelectricity via a metastable atomic
distortion pattern.

Nature Communications, 2020; 11 (1) DOI: 10.1038/s41467-020-18741-w ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201015101832.htm

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