MilesZhao / ecloud
Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor …
☆12Updated 4 years ago
Alternatives and similar repositories for ecloud:
Users that are interested in ecloud are comparing it to the libraries listed below
- code package with elemental property dictionary that trains a model based on training dataset and gives prediction on new perovskite comp…☆26Updated 6 years ago
- Expanded dataset of mechanical properties and observed phases of multi-principal element alloys☆32Updated 2 years ago
- MLMD: a programming-free AI platform to predict and design materials☆61Updated last month
- This is the code for the paper 'Machine learning-enabled high-entropy alloy discovery'☆70Updated last year
- Predict materials properties using only the composition information!☆101Updated last year
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆41Updated 2 years ago
- Generative deep learning model for inorganic materials☆18Updated 2 years ago
- Generate random alloys and compute various properties☆53Updated 4 months ago
- Modules for cross validation, evaluation and plot of SISSO☆15Updated 5 years ago
- Code to help you get started using machine learning in materials science☆17Updated 5 years ago
- Python interface to the SISSO (Sure Independence Screening and Sparsifying Operator) method.☆54Updated 11 months ago
- Accelerated Design of Layered Materials with Bayesian Optimization☆18Updated 6 years ago
- Inorganic Reaction Prediction☆12Updated 8 months ago
- Deep learning for crystal-structure recognition and analysis of atomic structures☆40Updated last year
- Supporting materials for "An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts".☆27Updated 5 years ago
- Comparative Analysis of Machine Learning Approaches on the Prediction of the Electronic Properties of Perovskite: A Case Study of the ABX…☆23Updated 3 years ago
- Crystal Graph Convolutional Neural Networks tutorial☆23Updated 2 years ago
- Machine learning model for crystal lattice constant prediction☆14Updated 4 years ago
- A script to build reference datasets for training neural network potentials from given LAMMPS trajectories.☆39Updated last week
- A Python library to calculate elastic properties of materials.☆57Updated 2 years ago
- Quantum-Wise VNL Application for Perovskite Building and Machine Learning☆10Updated 4 years ago
- This module uses Crystal Structure Prototype Database (CSPD) to generate a list of crystal structures for the system defined by user.☆18Updated 6 years ago
- ☆16Updated last month
- Chemically Directed Atom Swap Hopping -- Crystal structure prediction by swapping atoms in unfavourable chemical environments☆22Updated last year
- Python API wrapping the AFLUX API language for AFLOW library.☆25Updated 3 years ago
- Mirror of http://zeoplusplus.org/☆9Updated 6 years ago
- ☆89Updated 2 months ago
- Plots for "Machine-learned and codified synthesis parameters of oxide materials" in the journal Scientific Data☆13Updated 7 years ago
- image-based generative model for inverse design of solid state materials☆39Updated 3 years ago
- ☆85Updated 9 years ago