Computational modeling of grain boundary segregation: A review

C Hu, R Dingreville, BL Boyce - Computational Materials Science, 2024 - Elsevier
Nearly all metals, alloys, ceramics, and their associated composites are polycrystalline in
nature, with grain boundaries that separate well-defined crystalline regions that influence …

Genetic algorithm-guided deep learning of grain boundary diagrams: addressing the challenge of five degrees of freedom

C Hu, Y Zuo, C Chen, SP Ong, J Luo - Materials today, 2020 - Elsevier
Grain boundaries (GBs) often control the processing and properties of polycrystalline
materials. Here, potentially transformative research is represented by constructing GB …

Accelerating the development of multi-component Cu-Al-based shape memory alloys with high elastocaloric property by machine learning

XP Zhao, HY Huang, C Wen, YJ Su, P Qian - Computational Materials …, 2020 - Elsevier
Exploring elastocaloric materials with high transformation entropy change (ΔS) is a key
mission for the development of elastocaloric refrigeration technology. Here, we show an …

Machine learning approaches for ELNES/XANES

T Mizoguchi, S Kiyohara - Microscopy, 2020 - academic.oup.com
Materials characterization is indispensable for materials development. In particular,
spectroscopy provides atomic configuration, chemical bonding and vibrational information …

Genetic algorithm assisted multiscale modeling of grain boundary segregation of Al in ZnO and its correlation with nominal dopant concentration

N Yadav, SC Parker, A Tewari - Journal of the European Ceramic Society, 2024 - Elsevier
Grain boundary (GB) segregation of Al in ZnO plays an important role in lowering its thermal
conductivity for thermoelectric applications. However, the effect of Al concentration on the …

[HTML][HTML] Recent Progress in Nanostructured Functional Materials and Their Applications II

T Yamamoto, M Yoshiya, HN Nhat - Materials Transactions, 2023 - jstage.jst.go.jp
Microstructure of the materials is essential to design new functional materials, especially
from atomic scale to micron order structures. Many attempts have been carried out to give …

Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search

S Kiyohara, T Mizoguchi - The Journal of chemical physics, 2018 - pubs.aip.org
Non-stoichiometric structure localized at the grain boundary, namely, segregations of
impurities, dopants, and vacancies, has an important effect on a broad variety of material …

Energetically-favorable distribution of oxygen vacancies and metal atoms in perovskite BaCexZr0. 85− xY0. 15O2. 925 solid solutions using a genetic algorithm and …

IG Choi, Y Kim, KY Kim, J Jo, SJ Song… - Computational Materials …, 2019 - Elsevier
We studied the energetically-favorable distribution of oxygen vacancies and metal atoms in
perovskite BaCe x Zr 1− x Y 0.15 O 2.925 solid solution structures using a genetic algorithm …

Autonomous science: big data tools for small data problems in chemistry

AC Geiger, Z Cao, Z Song, JRW Ulcickas, GJ Simpson - 2020 - books.rsc.org
Arguably, the greatest opportunities to capitalize on machine learning advances lie at the
interface between data science and measurement science; algorithms can inform the choice …

A first-principles and machine learning combined method to investigate the interfacial friction between corrugated graphene

Z Liu, X Zhao, H Wang, Y Ma, L Gao… - … and Simulation in …, 2021 - iopscience.iop.org
Simulating the frictional properties of complex interfaces is computational resource
consuming. In this paper, we propose a density functional theory (DFT) calculation …