Mlatticeabc: generic lattice constant prediction of crystal materials using machine learning
Lattice constants such as unit cell edge lengths and plane angles are important parameters
of the periodic structures of crystal materials. Predicting crystal lattice constants has wide …
of the periodic structures of crystal materials. Predicting crystal lattice constants has wide …
Accelerating materials discovery through machine learning: Predicting crystallographic symmetry groups
Predicting crystal structure from the chemical composition is one of the most challenging and
long-standing problems in condensed matter physics. This problem resides at the interface …
long-standing problems in condensed matter physics. This problem resides at the interface …
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
Geometric information such as the space groups and crystal systems plays an important role
in the properties of crystal materials. Prediction of crystal system and space group thus has …
in the properties of crystal materials. Prediction of crystal system and space group thus has …
Towards quantitative evaluation of crystal structure prediction performance
Crystal structure prediction (CSP) is now increasingly used in the discovery of novel
materials with applications in diverse industries. However, despite decades of …
materials with applications in diverse industries. However, despite decades of …
Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula
One of the most challenging problems in condensed matter physics is to predict crystal
structure just from the chemical formula of the material. In this work, we present a robust …
structure just from the chemical formula of the material. In this work, we present a robust …
Descriptors for predicting the lattice constant of body centered cubic crystal
The prediction of the lattice constant of binary body centered cubic crystals is performed in
terms of first principle calculations and machine learning. In particular, 1541 binary body …
terms of first principle calculations and machine learning. In particular, 1541 binary body …
CrySPY: a crystal structure prediction tool accelerated by machine learning
T Yamashita, S Kanehira, N Sato, H Kino… - … and Technology of …, 2021 - Taylor & Francis
We have developed an open-source software called CrySPY, which is a crystal structure
prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables …
prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables …
Efficient approximations of complete interatomic potentials for crystal property prediction
We study property prediction for crystal materials. A crystal structure consists of a minimal
unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive …
unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive …
[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 …
Material symmetry recognition and property prediction accomplished by crystal capsule representation
C Liang, Y Rouzhahong, C Ye, C Li, B Wang… - Nature …, 2023 - nature.com
Learning the global crystal symmetry and interpreting the equivariant information is crucial
for accurately predicting material properties, yet remains to be fully accomplished by existing …
for accurately predicting material properties, yet remains to be fully accomplished by existing …