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Materials Learning Algorithms
MALA (Materials Learning Algorithms) is a data-driven framework to accelerate electronic structure calculations based on machine learning. Its purpose is to construct neural-network surrogate models for bypassing computationally expensive steps in state-of-the-art density functional simulations.
- DFT
- machine-learning
- Python
- Fortran
- Shell
- + 1
11
114
FLEUR
FLEUR is a feature-full, freely available FLAPW (full-potential linearized augmented-plane-wave) code, based on density-functional theory. This highly precise all-electron approach is universally applicable to all atoms of the periodic table and to systems with compact as well as open structures.
- DFT
- High performance computing
- hpc
- + 3
- Fortran Free Form
- C
- Motorola 68K Assembly
- + 2
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