SINGROUP / dscribeLinks
DScribe is a python package for creating machine learning descriptors for atomistic systems.
☆436Updated 6 months ago
Alternatives and similar repositories for dscribe
Users that are interested in dscribe are comparing it to the libraries listed below
Sorting:
- An open-source Python package for creating fast and accurate interatomic potentials.☆327Updated 3 weeks ago
- Graph deep learning library for materials☆354Updated last week
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆415Updated 2 weeks ago
- n2p2 - A Neural Network Potential Package☆235Updated 3 months ago
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆312Updated 2 months ago
- ANI-1 neural net potential with python interface (ASE)☆224Updated last year
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals☆534Updated 2 years ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆759Updated this week
- SchNetPack - Deep Neural Networks for Atomistic Systems☆847Updated this week
- A code to generate atomic structure with symmetry☆316Updated this week
- libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io☆364Updated last month
- molSimplify code☆190Updated 2 weeks ago
- i-PI: a universal force engine☆262Updated last week
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆643Updated 5 months ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆403Updated this week
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆147Updated 11 months ago
- A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies☆290Updated 3 months ago
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆508Updated last week
- A toolkit for visualizations in materials informatics.☆232Updated this week
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆254Updated 2 weeks ago
- SchNet - a deep learning architecture for quantum chemistry☆249Updated 6 years ago
- atomate2 is a library of computational materials science workflows☆223Updated last week
- A Python package for manipulating atomistic data of software in computational science☆212Updated last week
- An automatic engine for predicting materials properties.☆156Updated last year
- Combining Psi4 and Numpy for education and development.☆368Updated last year
- The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation avai…☆344Updated this week
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆347Updated last week
- NequIP is a code for building E(3)-equivariant interatomic potentials☆743Updated this week
- Packmol - Initial configurations for molecular dynamics simulations☆276Updated this week
- Accurate Neural Network Potential on PyTorch☆498Updated 7 months ago