jagar2 / Revealing-Ferroelectric-Switching-Character-Using-Deep-Recurrent-Neural-Networks
The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep…
☆22Updated last year
Related projects ⓘ
Alternatives and complementary repositories for Revealing-Ferroelectric-Switching-Character-Using-Deep-Recurrent-Neural-Networks
- Machine Learning Package Targeted for Amorphous Materials.☆19Updated 3 years ago
- Jupyter notebooks describing data analysis procedures for my published/submitted papers☆11Updated 3 years ago
- Accelerated Design of Layered Materials with Bayesian Optimization☆16Updated 5 years ago
- Gaussian processes and Bayesian optimization for images and hyperspectral data☆57Updated 11 months ago
- Deep learning for crystal-structure recognition and analysis of atomic structures☆40Updated 9 months ago
- MAterials Simulation Toolkit for Machine Learning (MAST-ML)☆105Updated last month
- Framework for storing, visualizing, and processing Universal Spectroscopic and Imaging Data (USID)☆25Updated 6 months ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆37Updated last year
- The materials for the Fall ML in Materials course at the UTK MSE☆82Updated 11 months ago
- Code to help you get started using machine learning in materials science☆15Updated 5 years ago
- Data Science for Materials Science☆58Updated 9 months ago
- Spring 2023 seminar on automated experiment☆23Updated last year
- Python code to identify and calculate decomposition of materials using Raman spectroscopy☆23Updated 3 years ago
- Molecular Simulation with Machine Learning - On-line workshop, July 13-14, 2020☆23Updated 4 years ago
- Expanded dataset of mechanical properties and observed phases of multi-principal element alloys☆31Updated 2 years ago
- an automatic plot digitizer for spectroscopy images (i.e. XANES and Raman)☆30Updated 2 years ago
- ☆16Updated 4 years ago
- Deep and machine learning for atomic-scale and mesoscale data☆12Updated 9 months ago
- A Python library for building atomic neural networks☆107Updated this week
- Data-driven risk-conscious thermoelectric materials discovery☆14Updated last month
- Python library written in C++ for calculation of local atomic structural environment☆57Updated 2 months ago
- ☆14Updated 2 years ago
- ☆121Updated last year
- This package provides the interface module between aenet [1] and LAMMPS [2], patch of aenet for the LAMMPS library, and Artificial Neural…☆17Updated last year
- Automatic XRD classification for thin-film materials using CNNs, Class Activation Maps and Data Augmentation☆50Updated 4 years ago
- Machine learning model for complex concentrated alloys/high entropy alloys using TensorFlow☆14Updated 3 years ago
- A Python library to calculate elastic properties of materials.☆53Updated 2 years ago
- A shell script for calculating many interesting materials' parameters for photovoltaic applications, includng Band gap, polarisation☆21Updated 9 years ago
- ☆16Updated last week
- A wrapper for many computational codes of thermal conductivity☆22Updated 2 years ago