Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
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Updated
Jul 10, 2024 - Python
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
NequIP is a code for building E(3)-equivariant interatomic potentials
Multidimensional data analysis
FiPy is a Finite Volume PDE solver written in Python
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.
A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.
Graph deep learning library for materials
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
Deep and Machine Learning for Microscopy
Heavyweight plotting tools for ab initio calculations
[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A toolkit for visualizations in materials informatics.
atomate2 is a library of computational materials science workflows
Electronic transport properties from first-principles calculations
RadonPy is a Python library to automate physical property calculations for polymer informatics.
Open-source library for analyzing the results produced by ABINIT
Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)
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