Alternatives and similar repositories for ASPH-Code

Users that are interested in ASPH-Code are comparing it to the libraries listed below

• by256 / icsg3d
  3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning (JCIM 2020)
  ☆41Updated 2 years ago
• Tsotsalas-Group / MOF_Literature_Extraction
  ☆30Updated 3 years ago
• sparks-baird / matbench-genmetrics
  Generative materials benchmarking metrics, inspired by guacamol and CDVAE.
  ☆38Updated last year
• kaist-amsg / Composition-Conditioned-Crystal-GAN
  Composition-Conditioned Crystal GAN pytorch code
  ☆43Updated 3 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
• CompRhys / roost
  Representation Learning from Stoichiometry
  ☆59Updated 2 years ago
• Tinystormjojo / geo-CGNN
  ☆33Updated 10 months ago
• argonne-lcf / active-learning-md
  Active learning workflow developed as a part of the upcoming article "Machine Learning Inter-Atomic Potentials Generation Driven by Activ…
  ☆27Updated 3 years ago
• ruihwang / MOF-CGCNN
  We developed a novel method, MOF-CGCNN, to efficiently and accurately predict the methane the volumetric uptakes at 65 bar for MOFs. Two …
  ☆19Updated 3 years ago
• kaist-amsg / imatgen
  image-based generative model for inverse design of solid state materials
  ☆40Updated 3 years ago
• zhenpengyao / Supramolecular_VAE
  ☆32Updated 4 years ago
• nityasagarjena / PaiNN-model
  This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials
  ☆19Updated 2 years ago
• JoshRackers / equivariant_electron_density
  Generate and predict molecular electron densities with Euclidean Neural Networks
  ☆48Updated last year
• gomes-lab / Mat2Spec
  ☆28Updated 3 years ago
• ulissigroup / finetuna
  Active Learning for Machine Learning Potentials
  ☆55Updated last year
• Qi-max / amlearn
  Machine Learning Package Targeted for Amorphous Materials.
  ☆19Updated 4 years ago
• sbanik2 / CEGANN
  Crystal Edge Graph Attention Neural Network
  ☆22Updated 11 months ago
• SeyedMohamadMoosavi / tools-cp-porousmat
  Heat capacity predictor for porous materials
  ☆12Updated 11 months ago
• libAtoms / workflow
  python workflow toolkit
  ☆39Updated 3 months ago
• vxfung / MatDeepLearn_DOS
  MatDeepLearn for DOS prediction
  ☆24Updated 2 years ago
• RishikeshMagar / OGCNN
  Crystal graph convolutional neural networks for predicting material properties.
  ☆33Updated 2 years ago
• GOMC-WSU / py-MCMD
  This python code creates hybrid MD/MC (NAMD/GOMC) simulations for the NVT, NPT, GCMC, and GEMC-NVT ensembles
  ☆38Updated last month
• MilesZhao / PGCGM
  Source code for generating materials with 20 space groups using PGCGM
  ☆33Updated 2 years ago
• LePingKYXK / Velocity-ACF
  Calculate Spectrum Based on Fast Fourier Transform (FFT) of the Velocity Autocorrelation Function (VACF).
  ☆26Updated last year
• richardjgowers / zeoplusplus
  Mirror of http://zeoplusplus.org/
  ☆9Updated 7 years ago
• CJBartel / TestStabilityML
  ☆26Updated last year
• tummfm / difftre
  Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
  ☆31Updated last year
• atomisticnet / MLP-beginners-guide
  Strategies for the Construction of Neural-Network Based Machine-Learning Potentials (MLPs)
  ☆28Updated 4 years ago
• mogroupumd / Topological_Analysis
  Topological analysis for Li local space, site, pathway in crystal structures
  ☆13Updated 5 years ago
• imagdau / aseMolec
  ☆25Updated last week