Pravanop / Perovskite-PredictionLinks
Predicting new perovskites with ensemble Machine Learning algorithms
☆15Updated last month
Alternatives and similar repositories for Perovskite-Prediction
Users that are interested in Perovskite-Prediction are comparing it to the libraries listed below
Sorting:
- code package with elemental property dictionary that trains a model based on training dataset and gives prediction on new perovskite comp…☆29Updated 7 years ago
- Predict the band gap energy for inorganic materials☆19Updated last year
- A collection of notebooks in support of the publication "A Database of Experimentally Measured Lithium Solid Electrolyte Conductivities E…☆21Updated 2 years ago
- Comparative Analysis of Machine Learning Approaches on the Prediction of the Electronic Properties of Perovskite: A Case Study of the ABX…☆26Updated 3 years ago
- Machine Learning for Catalysis☆20Updated 2 years ago
- Machine Learning for Catalyst Design and Discovery☆17Updated 6 years ago
- Quantum-Wise VNL Application for Perovskite Building and Machine Learning☆11Updated 5 years ago
- polyVERSE is a comprehensive repository of informatics-ready datasets curated by the Ramprasad Group.☆29Updated last month
- Repository for predicting conductivities through Arrhenius parameters for polymer electrolytes.☆23Updated last year
- ☆37Updated 4 years ago
- A knowledge graph unifying computational and experimental data for MOFs☆35Updated last month
- MatDesign: a programming-free AI platform to predict and design materials☆76Updated this week
- Supporting materials for "An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts".☆28Updated 6 years ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆42Updated 2 years ago
- The functions of superalloyDigger toolkit include batch downloading documents in XML and TXT format from the Elsevier database, locating …☆66Updated 6 months ago
- Python interface to the SISSO (Sure Independence Screening and Sparsifying Operator) method.☆63Updated last year
- Applying Machine Learning methodologies in search of novel MOF's and battery materials.☆13Updated 2 years ago
- Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials property prediction☆83Updated last year
- Expanded dataset of mechanical properties and observed phases of multi-principal element alloys☆42Updated 3 years ago
- Physics Informed Neural Network constrained to follow Gibbs Free Energy Equation☆14Updated 5 months ago
- Tools for auto-generating the battery-materials database.☆48Updated 3 years ago
- ☆25Updated last year
- BatteryDataExtractor: battery-aware text-mining software embedded with BERT models.☆21Updated 2 years ago
- Extracts tables into json format from HTML/XML files☆39Updated 5 years ago
- Predict materials properties using only the composition information!☆119Updated 2 years ago
- Scalable graph neural networks for materials property prediction☆63Updated last year
- Machine learning model for crystal lattice constant prediction☆14Updated 4 years ago
- Plots for "Machine-learned and codified synthesis parameters of oxide materials" in the journal Scientific Data☆14Updated 8 years ago
- data and code to reduplicate paper: Topological representations of crystalline compounds for the machine-learning prediction of materials…☆22Updated 4 years ago
- We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …☆21Updated 4 years ago