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@aimat-lab Artificial Intelligence for Materials Science group

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    29 repositories

    • graph_attention_student

      Public
      Minimal implementation of graph attention student model architecture
      Python
      •1•5•1•0•Updated Oct 7, 2024Oct 7, 2024

    • megan_global_explanations

      Public
      Extracting global concept explanations from the self-explaining MEGAN model
      Python
      •
      MIT License
      •1•2•1•1•Updated Sep 13, 2024Sep 13, 2024

    • xrdpattern

      Public
      Python library for XrdPatterns including file import, file export and postprocessing functionalities
      diffraction-patternpowder-diffraction
      C++
      •
      GNU Lesser General Public License v2.1
      •0•2•0•0•Updated Sep 4, 2024Sep 4, 2024

    • CrystalStructure

      Public
      A python implementation of a Crystal Structure based on pymatgen
      Python
      •0•0•0•0•Updated Sep 4, 2024Sep 4, 2024

    • opXRD-paper

      Public
      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.
      Python
      •
      MIT License
      •0•1•0•0•Updated Jul 5, 2024Jul 5, 2024

    • visual_graph_datasets

      Public
      Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations of XAI methods
      Python
      •2•2•1•0•Updated Jun 26, 2024Jun 26, 2024

    • coarse-graining-AL

      Public
      Code for the paper "Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations" (ICML 2024)
      active-learningcoarse-grainingnormalizing-flow
      Python
      •
      MIT License
      •0•3•0•0•Updated May 24, 2024May 24, 2024

    • gcnn_keras

      Public
      Graph convolutions in Keras with TensorFlow, PyTorch or Jax.
      machine-learninggraph-algorithmsgraphsneural-networksnetworksmoleculesgraph-networksgraph-convolutionragged-tensors
      Jupyter Notebook
      •
      MIT License
      •30•108•12•0•Updated May 6, 2024May 6, 2024

    • 3DSC

      Public
      Repo for the paper publishing the superconductor database with 3D crystal structures.
      Jupyter Notebook
      •
      Other
      •4•15•2•0•Updated Jan 8, 2024Jan 8, 2024

    • perovskite_htm_screening

      Public
      Code for paper "Accelerating the discovery of materials for integrated/complex device"
      Python
      •
      MIT License
      •1•3•0•0•Updated Dec 20, 2023Dec 20, 2023

    • llm-data-extraction

      Public
      Using Large Language Models for Zero-Shot Data Extraction from Scientific Literature
      TeX
      •
      GNU Affero General Public License v3.0
      •1•0•0•0•Updated Dec 5, 2023Dec 5, 2023

    • ChemEngML

      Public
      Fortran
      •
      MIT License
      •1•2•0•0•Updated Nov 10, 2023Nov 10, 2023

    • MAChINE

      Public
      Client-Server Web App to introduce usage of ML in materials science to beginners
      JavaScript
      •
      MIT License
      •1•1•9•1•Updated Sep 29, 2023Sep 29, 2023

    • ChemMatData

      Public
      An overview over chemical datasets and where to find them
      2•17•1•1•Updated Aug 29, 2023Aug 29, 2023

    • jarvis_leaderboard

      Public
      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.
      Jupyter Notebook
      •
      Other
      •43•0•0•0•Updated Aug 7, 2023Aug 7, 2023

    • microdroplet_segmentation

      Public
      Segmentation for microdroplet arrays used in screening experiments.
      Python
      •
      MIT License
      •0•1•0•0•Updated Jul 19, 2023Jul 19, 2023

    • ML4pXRDs

      Public
      Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.
      machine-learninghigh-throughputautomated-analysisxrdpowderdiffractograms
      Python
      •
      MIT License
      •0•0•0•0•Updated Jul 14, 2023Jul 14, 2023

    • NNsForMD

      Public
      Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.
      machine-learningneural-networkmolecular-dynamicspotentialsexcited-statesab-initio-simulations
      Python
      •
      MIT License
      •6•10•0•0•Updated Nov 10, 2022Nov 10, 2022

    • package-index

      Public
      Public package index for the AIMat Lab.
      HTML
      •0•0•0•0•Updated Nov 8, 2022Nov 8, 2022

    • cluster-hyperopt-tutorial

      Public
      Python
      •1•0•0•0•Updated Sep 27, 2022Sep 27, 2022

    • MOF_web_interface

      Public
      Python
      •
      MIT License
      •1•2•0•0•Updated May 9, 2022May 9, 2022

    • ActiveLearningFramework-1

      Public
      Bachelor Thesis of Meret Unbehaun, Topic: Active Learning strategies for Machine Learned potentials
      Python
      •3•0•0•0•Updated Apr 14, 2022Apr 14, 2022

    • ActiveLearningFramework

      Public
      Bachelor Thesis of Meret Unbehaun, Topic: Active Learning strategies for Machine Learned potentials
      Python
      •3•4•0•0•Updated Apr 14, 2022Apr 14, 2022

    • GraphReactionNet

      Public
      0•0•0•0•Updated Apr 10, 2022Apr 10, 2022

    • smilesDrawer

      Public
      A small, highly performant JavaScript component for parsing and drawing SMILES strings. Released under the MIT license.
      JavaScript
      •
      MIT License
      •71•0•0•0•Updated Feb 25, 2022Feb 25, 2022

    • MOF_Synthesis_Prediction

      Public
      Jupyter Notebook
      •
      MIT License
      •3•29•0•0•Updated Dec 16, 2021Dec 16, 2021

    • ML4HEOs

      Public
      Machine Learning methods for high entropy oxides
      Python
      •
      MIT License
      •0•4•0•0•Updated Oct 29, 2021Oct 29, 2021

    • MOF_Literature_Extraction

      Public
      HTML
      •
      MIT License
      •6•0•0•0•Updated Aug 3, 2021Aug 3, 2021

    • AutoMol

      Public
      AutoML for chemistry/materials
      Jupyter Notebook
      •0•0•0•0•Updated Apr 21, 2021Apr 21, 2021