Nanotechnologies / Materials
Energy, Environmental, New materials

Development and application of data-driven simulation
techniques for interface chemical processes
Department of Precision Engineering, Graduate School of Engineering
Professor Yoshitada Morikawa
https://researchmap.jp/read0118585?lang=en

Nanotechnologies / Materials

Abstract
It is extremely important not only for fundamental points of view
but also from application points of view to elucidate atomic structures, electronic properties, and chemical reaction processes atinterfaces at the atomic level. Density functional theory (DFT) cancalculate the atomic structures and electronic states at the atomic
and molecular level with extremely high accuracy, but the calculations are very heavy, and the spatial and time scales of systems
that can be handled are restricted to several nanometers, several
tens of ps, respectively. On the other hand, by using empiricalinteratomic potentials, it is possible to elucidate phenomena withspatial sizes of sub μm and time scales of μs. By constructing
machine-learned interatomic potentials that fit calculated results
by DFT using state-of-the-art machine-learning techniques, larger-scale simulations with near-DFT accuracy become possible.
In this research, we aim to elucidate various interfacial reactions
using this method and to propose design principles or more desirable interfacial reactions.

▍ Background & Results
Highly accurate DFT calculations have played an extremely
important role, but calculations are very heavy, so even with thelatest supercomputers, it is necessary to use models with limited
assumptions. Recent advances in machine learning methods havemade it possible to derive highly accurate interatomic potentials
from the results of DFT calculations for various structures. Interfacial reactions can be simulated under a more realistic device environment, and it is expected that in the future it will be possible topredict reaction processes and to design more desirable materials.

▍ Significance of the research and Future perspective

interfaces at the atomic level is very important not only from fundamental scientific points of view, but is also useful for various
applications such as heterogeneous catalysts, semiconductor
devices, crystal growth, etching, fuel cells, secondary batteries,
wear, and lubrication. However, it is often difficult to elucidate themexperimentally. Therefore, elucidation at the atomic level by DFT
calculations with high accuracy plays an extremely important role.
However, due to the huge amount of calculations, the spatial scaleof the system that can be calculated was limited to several nanometers, and the time scale was limited to several tens of ps. By fitting
the DFT calculation results to the atomic force potential using stateof-the-art machine learning techniques, we are able to perform
highly accurate calculations very quickly. By using this tequnique,we have clarified Cu-Zn surface alloying process, which are important in catalytic methanol synthesis. We clarified the elementary
processes of the alloy formation by performing molecular dynamics simulations with space scales of sub-μm and time scales of μs
using machine learning potentials. As a result, it was clarified thatalloying progresses due to frequent exchange of atoms between
the upper and lower step edge atoms near the atomic steps on thesolid surface. This is qualitatively consistent with experimental observations using STM, but reveals that atomic processes frequently
occurring are quite different from the experimentally proposed alloy
formation. In this way, simulations based on machine learning potentials are extremely effective in elucidating elementary processes
at the interface at the atomic level.
We will apply this effective method to various systems suchas the surface oxidation etching process of diamond, which isimportant as a material for cutting tools and is expected to be anext-generation semiconductor for power and quantum devices,
as well as other important catalysts and fuel cell electrode reaction
processes.

Elucidating atomic structures, electronic properties, and chemical reaction processes at gas/solid, liquid/solid, or solid/solid

P a t e n tHalim, H.H ; Morikawa, Y. The Elucidation of Cu-Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics, ACS Physical Chemistry Au,
2022, 2, 430-447. doi: 10.1021/acsphyschemau.2c00017
UR
L http://www-cp.prec.eng.osaka-u.ac.jp/#showalumnia
K e y w o r d density functional theory, machine learning, surface science, heterogeneous catalysis, electrochemistry
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