Band structure unfolding made easy!
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Updated
Jun 22, 2024 - Python
Band structure unfolding made easy!
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.
A SEAMM plug-in for building periodic boxes of fluid using Packmol
Molsystem provides a general class for handling molecular and periodic systems
atomRDF is a python tool for ontology-based creation, manipulation, and quering of structures. atomRDF uses the Computational Material Sample Ontology (CMSO).
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
doped is a Python software for the generation, pre-/post-processing and analysis of defect supercell calculations, implementing the defect simulation workflow in an efficient, reproducible, user-friendly yet powerful and fully-customisable manner.
Package to perform automatic bonding analysis with the program Lobster in the field of computational materials science and quantum chemistry
AiiDA plugin of the high-performance density functional theory code FLEUR (www.judft.de) for high-throughput electronic structure calculations.
Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
Full-potential Linearized Augmented Plane Wave code FLEUR: All-electron DFT (repo mirror)
A collection of interactive notebooks to explain concepts of quantum mechanics and related topics
Peridynamics with the Cabana library
Package providing base types for representing/manipulating periodic crystal structures.
Utility classes and functions that support other MolSSI tools
Notes of Computational Materials Science: From Algorithm principle to Code Implementation
Post-processing toolkit for electronic structure calculations
A tool for streamlining data analysis and visualisation for thermoelectrics and charge carrier transport in computational materials science.
The core of the SEAMM environment and graphical interface.
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