Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
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Updated
May 31, 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
Density-functional toolkit
Curated list of known efforts in materials informatics = modern materials science
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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