High‐throughput discovery of novel cubic crystal materials using deep generative neural networks
High‐throughput screening has become one of the major strategies for the discovery of
novel functional materials. However, its effectiveness is severely limited by the lack of …
novel functional materials. However, its effectiveness is severely limited by the lack of …
Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
Graph neural networks (GNNs) have recently been used to learn the representations of
crystal structures through an end-to-end data-driven approach. However, a systematic top …
crystal structures through an end-to-end data-driven approach. However, a systematic top …
[HTML][HTML] MaterialsAtlas. org: a materials informatics web app platform for materials discovery and survey of state-of-the-art
The availability and easy access of large-scale experimental and computational materials
data have enabled the emergence of accelerated development of algorithms and models for …
data have enabled the emergence of accelerated development of algorithms and models for …
Deep learning-based prediction of contact maps and crystal structures of inorganic materials
Crystal structure prediction is one of the major unsolved problems in materials science.
Traditionally, this problem is formulated as a global optimization problem for which global …
Traditionally, this problem is formulated as a global optimization problem for which global …
Machine learning-based deoxidizer screening for intensified hydrogen production from steam splitting
The design of advanced deoxidizer is the key to promote hydrogen production from
chemical looping steam splitting, however, the deoxidizer shows complicated possibility of …
chemical looping steam splitting, however, the deoxidizer shows complicated possibility of …
Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component …
Low lattice thermal conductivity is essential for high thermoelectric performance of a
material. Lattice thermal conductivity is often computed using density functional theory …
material. Lattice thermal conductivity is often computed using density functional theory …
[PDF][PDF] Automated prediction of lattice parameters from X-ray powder diffraction patterns
A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate
determination of unit-cell lattice parameters. This step often requires significant human …
determination of unit-cell lattice parameters. This step often requires significant human …
[HTML][HTML] Modeling of lattice parameters of cubic perovskite oxides and halides
Y Zhang, X Xu - Heliyon, 2021 - cell.com
Perovskites having the chemical formulae of ABX 3 are promising candidates for various
electronic, magnetic, and thermal applications. One of the important structural factors is a …
electronic, magnetic, and thermal applications. One of the important structural factors is a …
Machine learning based prediction of space group for Ba (Ce0. 8-xZrx) Y0. 2O3 perovskite-type protonic conductors
K Nomura, H Shimada, Y Yamaguchi, H Sumi… - Ceramics …, 2023 - Elsevier
Abstract Space groups of Ba (Ce 0.8-x Zr x) Y 0.2 O 3 (0≤ x≤ 0.8) perovskite-type protonic
conductors of wide temperature range from 25 to 900° C in dry and wet atmospheres are …
conductors of wide temperature range from 25 to 900° C in dry and wet atmospheres are …
[HTML][HTML] Predicting the crystal structure and lattice parameters of the perovskite materials via different machine learning models based on basic atom properties
Perovskite materials have high potential for the renewable energy sources such as solar PV
cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice …
cells, fuel cells, etc. Different structural distortions such as crystal structure and lattice …