Machine learning in materials genome initiative: A review

Y Liu, C Niu, Z Wang, Y Gan, Y Zhu, S Sun… - Journal of Materials …, 2020 - Elsevier
Discovering new materials with excellent performance is a hot issue in the materials
genome initiative. Traditional experiments and calculations often waste large amounts of …

Deep yellow emission and high energy device applications of copper doped orthorhombic zirconium titanate nanoparticles

S Akshay, YS Vidya, HC Manjunatha… - Materials Today …, 2023 - Elsevier
For the first time, Zirconium titanate doped with copper (1–9 mol%) was synthesized by an
aloe vera-mediated combustion route followed by calcination at 700 o C. The high …

High energy devices and display technology applications of silver doped zirconium titanate nanoparticles

S Akshay, YS Vidya, HC Manjunatha… - Materials Chemistry and …, 2023 - Elsevier
For the first of its kind, Silver activated (1–9 mol%) Zirconium Titanate nanoparticles (NPs)
have been synthesized by green solution combustion method using AloeV era gel extract as …

Machine learning based analysis of metal support co-sintering process for solid oxide fuel cells

W Shin, Y Yamaguchi, M Horie, H Shimada… - Ceramics …, 2023 - Elsevier
Porous metals are promising substrate supports of solid oxide fuel cell for the applications of
mobile and high-power density. The process conditions for a suitable mixture of pore former …

Modeling of the sintered density in Cu-Al alloy using machine learning approaches

S Asnaashari, M Shateri, A Hemmati-Sarapardeh… - ACS …, 2023 - ACS Publications
In powder metallurgy materials, sintered density in Cu-Al alloy plays a critical role in
detecting mechanical properties. Experimental measurement of this property is costly and …

Performance investigation of fire protection and intervention strategies: Artificial neural network-based assessment framework

R Ouache, KM Nahiduzzaman, K Hewage… - Journal of Building …, 2021 - Elsevier
This study aims to develop an integrated framework to investigate and assess fire safety
strategies, including protection and intervention. The proposed framework is also able to …

Machine leaning aided study of sintered density in Cu-Al alloy

Z Deng, H Yin, X Jiang, C Zhang, K Zhang… - Computational Materials …, 2018 - Elsevier
The mechanical properties of powder metallurgy (PM) materials are closely related to their
density. In this case we demonstrate an approach of utilizing machine-learning algorithms …

Optimizing the Properties of Metakaolin-based (Na, K)-Geopolymer Using Taguchi Design Method

A Al-dujaili, IA Disher Al-hydary, Z Zayer Hassan - International Journal of …, 2020 - ije.ir
Geopolymer paste is an innovative construction material which shall be produced by
chemical action of inorganic molecules. It is a more environmentally friendly alternative to …

Transfer learning aid the prediction of sintering densification

W Zhouzhi, Z Xiaomin, Z Zhipeng, Z Hengjia… - Ceramics …, 2020 - Elsevier
In powder metallurgy engineering, the master sintering curve (MSC) is crucial for estimating
the mechanical properties of sintered products and optimizing sintering process parameters …

Impacts of low sintering temperature on microstructure, atomic bonds, and dielectric constant of barium titanate (BaTiO3) prepared by co-precipitation technique

B Suherman, F Nurosyid, DK Sandi… - Journal of Physics …, 2022 - iopscience.iop.org
Abstract Barium Titanate (BaTiO 3 or BT) is one of which the most attractive ferroelectric
materials that have been widely studied. The fabrication process affects the properties of …