MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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.
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
A Virtual Machine for computational materials science
Band structure unfolding made easy!
A tool for streamlining data analysis and visualisation for thermoelectrics and charge carrier transport in computational materials science.
A workflow to create computation-ready metal-organic framework database.
Grand canonical optimization of grain boundary phases.
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.
IANN (InterAtomic Neural Network Framework) is an equivariant interatomic neural network potential framework package
Post-processing toolkit for electronic structure calculations
A free and open platform for interactive benchmarking and simulations using machine learning interatomic potentials
The core of the SEAMM environment and graphical interface.
AiiDA plugin of the high-performance density functional theory code JuKKR (www.judft.de) for high-throughput electronic structure calculations.
AiiDA plugin of the high-performance density functional theory code FLEUR (www.judft.de) for high-throughput electronic structure calculations.
Framework to facilitate simulation workflows and experimental databases for additive manufacturing
Helpers for working with pymatgen structure graphs.
Physics-informed neural networks (PINNs) for forward simulation and inverse parameter estimation in 2D martensitic phase transformations.
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