Alternatives and similar repositories for MSE_Fall2024

Users that are interested in MSE_Fall2024 are comparing it to the libraries listed below

• SergeiVKalinin / MSE_Fall2023
  The materials for the Fall ML in Materials course at the UTK MSE
  ☆85Updated last year
• SergeiVKalinin / MSE_Spring2024
  The materials for the Spring Mathematics in Materials course at the UTK MSE
  ☆49Updated last year
• Fung-Lab / MatterTune
  A unified platform for fine-tuning atomistic foundation models in chemistry and materials science
  ☆41Updated this week
• WardLT / applied-ai-for-materials
  Course materials for "Applied AI for Materials Science and Engineering"
  ☆64Updated 3 years ago
• sparks-baird / mat_discover
  A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
  ☆44Updated 9 months ago
• sgbaird / honegumi
  Honegumi (骨組み) is an interactive "skeleton code" generator for API tutorials focusing on optimization packages.
  ☆54Updated last week
• jan-janssen / LangSim
  Application of Large Language Models (LLM) for computational materials science - visit jan-janssen.com/LangSim
  ☆63Updated 7 months ago
• aronwalsh / MLforMaterials
  Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
  ☆84Updated this week
• atomisticnet / MLP-beginners-guide
  Strategies for the Construction of Neural-Network Based Machine-Learning Potentials (MLPs)
  ☆28Updated 4 years ago
• SergeiVKalinin / AutomatedExperiment_Summer2023
  The summer training course on the Bayesian Optimization
  ☆27Updated last year
• TRI-AMDD / CAMD
  Agent-based sequential learning software for materials discovery
  ☆62Updated last year
• jla-gardner / graph-pes
  train and use graph-based ML models of potential energy surfaces
  ☆91Updated this week
• materials-data-facility / llm-resources
  ☆20Updated last year
• Kaaiian / Houston_ML_in_MSE_workshop
  Code to help you get started using machine learning in materials science
  ☆17Updated 6 years ago
• sparks-baird / xtal2png
  Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.
  ☆37Updated last year
• Quantum-Accelerators / template
  A template for Python packages. Developed by the @quantum-accelerators
  ☆59Updated this week
• SergeiVKalinin / MSE_Spring2025
  ☆35Updated 2 weeks ago
• hspark1212 / chemeleon
  A text-guided diffusion model for crystal structure generation
  ☆56Updated last week
• usnistgov / atomvision
  Deep learning framework for atomistic image data
  ☆34Updated 4 months ago
• WMD-group / ElementEmbeddings
  Python package to interact with high-dimensional representations of the chemical elements
  ☆43Updated this week
• materials-data-facility / awesome-bayesian-optimization
  ☆40Updated last year
• usnistgov / aims2024_workshop
  JARVIS-AIMS workshop
  ☆12Updated 10 months ago
• datalab-org / datalab
  datalab is a place to store experimental data and the connections between them.
  ☆63Updated this week
• autoatml / autoplex
  Code for automated fitting of machine learned interatomic potentials.
  ☆77Updated this week
• by256 / icsg3d
  3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)
  ☆41Updated 2 years ago
• CederGroupHub / alabos
  AlabOS: Managing the workflows in the Autonomous lab
  ☆44Updated this week
• dskoda / quests
  Quick Uncertainty and Entropy via STructural Similarity
  ☆44Updated 3 weeks ago
• ilyes319 / mace-tutorials
  Collection of tutorials to use the MACE machine learning force field.
  ☆46Updated 8 months ago
• blaiszik / awesome-matchem-datasets
  ☆143Updated last week
• SeyedMohamadMoosavi / tools-cp-porousmat
  Heat capacity predictor for porous materials
  ☆12Updated 11 months ago