MatGPT: A vane of materials informatics from past, present, to future
Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …
disciplines, materials informatics is continuously accelerating the vigorous development of …
Crystal diffusion variational autoencoder for periodic material generation
Generating the periodic structure of stable materials is a long-standing challenge for the
material design community. This task is difficult because stable materials only exist in a low …
material design community. This task is difficult because stable materials only exist in a low …
Crystal structure prediction by joint equivariant diffusion
Abstract Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While
CSP can be addressed by employing currently-prevailing generative models (eg diffusion …
CSP can be addressed by employing currently-prevailing generative models (eg diffusion …
Search methods for inorganic materials crystal structure prediction
X Yin, CE Gounaris - Current Opinion in Chemical Engineering, 2022 - Elsevier
Crystal structure prediction (CSP) is the problem of determining the most stable crystalline
arrangements of materials given their chemical compositions. In general, CSP …
arrangements of materials given their chemical compositions. In general, CSP …
Reflections on one million compounds in the open quantum materials database (OQMD)
Density functional theory (DFT) has been widely applied in modern materials discovery and
many materials databases, including the open quantum materials database (OQMD) …
many materials databases, including the open quantum materials database (OQMD) …
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 …
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 …
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 …
Space group constrained crystal generation
Crystals are the foundation of numerous scientific and industrial applications. While various
learning-based approaches have been proposed for crystal generation, existing methods …
learning-based approaches have been proposed for crystal generation, existing methods …
[图书][B] Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm
While crystal structure prediction (CSP) remains a longstanding challenge, we introduce
ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm …
ParetoCSP, a novel algorithm for CSP, which combines a multi-objective genetic algorithm …