mpeshel / Interpretable_AI_for_batteryLinks
Interpretable learning for electrode-voltage prediction and design of multivalent metal-ion batteries
☆11Updated 4 years ago
Alternatives and similar repositories for Interpretable_AI_for_battery
Users that are interested in Interpretable_AI_for_battery are comparing it to the libraries listed below
Sorting:
- Scalable graph neural networks for materials property prediction☆63Updated last week
- AI for crystal materials☆112Updated last week
- Composition-Conditioned Crystal GAN pytorch code☆42Updated 3 years ago
- BAMBOO (Bytedance AI Molecular BOOster) is an AI-driven machine learning force field designed for precise and efficient electrolyte simu…☆146Updated 2 months ago
- FTCP code☆36Updated 2 years ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆85Updated 4 years ago
- A system for rapid identification and analysis of metal-organic frameworks☆69Updated 2 months 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
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆26Updated last year
- This is a conditionally generative model for crystal structures based on a modified version of CDVAE.☆39Updated 3 months ago
- Universal Transfer Learning in Porous Materials, including MOFs.☆115Updated last year
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆25Updated 3 years ago
- Zeolite GAN☆25Updated 5 years ago
- ☆24Updated last year
- Crystal Edge Graph Attention Neural Network☆23Updated last year
- ☆29Updated 3 years ago
- [ICLR 2024] The implementation for the paper "Space Group Constrained Crystal Generation"☆61Updated 2 months ago
- ☆35Updated 3 years ago
- A repository for implementing graph network models based on atomic structures.☆104Updated last year
- Supporting material for the paper "Data driven collective variables for enhanced sampling"☆20Updated last year
- ☆15Updated 2 years ago
- Source code for generating materials with 20 space groups using PGCGM☆34Updated 3 years ago
- SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS☆138Updated 2 weeks ago
- ☆69Updated 4 years ago
- DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules☆30Updated last year
- Crystal graph convolutional neural networks for predicting material properties.☆33Updated 3 years ago
- Python library for the construction of porous materials using topology and building blocks.☆84Updated 8 months ago
- A Large Language Model of the CIF format for Crystal Structure Generation☆150Updated 4 months ago
- 3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)☆43Updated 2 years ago
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆31Updated 5 years ago