Pinned Repositories
3DSC
Repo for the paper publishing the superconductor database with 3D crystal structures.
ActiveLearningFramework
Bachelor Thesis of Meret Unbehaun, Topic: Active Learning strategies for Machine Learned potentials
ChemMatData
An overview over chemical datasets and where to find them
coarse-graining-AL
Code for the paper "Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations" (ICML 2024)
gcnn_keras
Graph convolutions in Keras with TensorFlow, PyTorch or Jax.
graph_attention_student
Minimal implementation of graph attention student model architecture
ML4HEOs
Machine Learning methods for high entropy oxides
MOF_Synthesis_Prediction
NNsForMD
Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.
perovskite_htm_screening
Code for paper "Accelerating the discovery of materials for integrated/complex device"
Artificial Intelligence for Materials Science group's Repositories
aimat-lab/gcnn_keras
Graph convolutions in Keras with TensorFlow, PyTorch or Jax.
aimat-lab/MOF_Synthesis_Prediction
aimat-lab/ChemMatData
An overview over chemical datasets and where to find them
aimat-lab/3DSC
Repo for the paper publishing the superconductor database with 3D crystal structures.
aimat-lab/NNsForMD
Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.
aimat-lab/graph_attention_student
Minimal implementation of graph attention student model architecture
aimat-lab/ActiveLearningFramework
Bachelor Thesis of Meret Unbehaun, Topic: Active Learning strategies for Machine Learned potentials
aimat-lab/ML4HEOs
Machine Learning methods for high entropy oxides
aimat-lab/coarse-graining-AL
Code for the paper "Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations" (ICML 2024)
aimat-lab/perovskite_htm_screening
Code for paper "Accelerating the discovery of materials for integrated/complex device"
aimat-lab/ChemEngML
aimat-lab/megan_global_explanations
Extracting global concept explanations from the self-explaining MEGAN model
aimat-lab/MOF_web_interface
aimat-lab/visual_graph_datasets
Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations of XAI methods
aimat-lab/xrdpattern
Python library for XrdPatterns including file import, file export and postprocessing functionalities
aimat-lab/MAChINE
Client-Server Web App to introduce usage of ML in materials science to beginners
aimat-lab/microdroplet_segmentation
Segmentation for microdroplet arrays used in screening experiments.
aimat-lab/opXRD-paper
Draft of a paper presenting and discussing a dataset that is currently being assembled by the AiMat group at KIT. It will be released for public and free use as soon as possible.
aimat-lab/ActiveLearningFramework-1
Bachelor Thesis of Meret Unbehaun, Topic: Active Learning strategies for Machine Learned potentials
aimat-lab/GraphReactionNet
aimat-lab/ML4pXRDs
Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.
aimat-lab/package-index
Public package index for the AIMat Lab.
aimat-lab/smilesDrawer
A small, highly performant JavaScript component for parsing and drawing SMILES strings. Released under the MIT license.
aimat-lab/AutoMol
AutoML for chemistry/materials
aimat-lab/cluster-hyperopt-tutorial
aimat-lab/CrystalStructure
A python implementation of a Crystal Structure based on pymatgen
aimat-lab/jarvis_leaderboard
This project provides benchmark-performances for materials science applications including Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Quantum Computation (QC) and Experiments (EXP) methods.
aimat-lab/llm-data-extraction
Using Large Language Models for Zero-Shot Data Extraction from Scientific Literature
aimat-lab/MOF_Literature_Extraction