Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
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Updated
Jun 14, 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.
A deep learning package for many-body potential energy representation and molecular dynamics
Multidimensional data analysis
Data mining for materials science
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
FiPy is a Finite Volume PDE solver written in Python
NequIP is a code for building E(3)-equivariant interatomic potentials
CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.
Catalyst Micro-kinetic Analysis Package for automated creation of micro-kinetic models used in catalyst screening
Open-source library for analyzing the results produced by ABINIT
DScribe is a python package for creating machine learning descriptors for atomistic systems.
Cross platform, open source application for the processing, visualization, and analysis of 3D tomography data
Curated list of known efforts in materials informatics = modern materials science
Density-functional toolkit
Heavyweight plotting tools for ab initio calculations
Materials Knowledge System in Python
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
Atomsk: A Tool For Manipulating And Converting Atomic Data Files -
Data Analysis program and framework for materials science data analytics, based on the managing framework SIMPL framework.
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