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All-electron, full-potential electronic structure theory for high-fidelity molecular and materials discovery.

FHI-aims (Fritz Haber Institute ab initio molecular simulations) is a leading all-electron electronic structure code based on numeric atom-centered orbitals (NAO). It is designed for high-performance, large-scale simulations of molecules and materials. In the 2026 landscape of AI for Science (AI4S), FHI-aims occupies a critical position as the primary high-fidelity data generator for training Machine Learning Interatomic Potentials (MLIPs) and Neural Network Force Fields. Unlike pseudopotential-based codes, FHI-aims treats all electrons explicitly, providing a benchmark-level accuracy that is essential for fine-tuning foundation models in materials discovery. The architecture is highly parallelized, optimized for exascale computing, and features a robust integration with the Atomic Simulation Environment (ASE). Its capability to handle hybrid functionals, many-body perturbation theory (GW, MP2), and relativistic effects across the periodic table makes it the preferred tool for researchers moving from traditional simulation to AI-integrated materials engineering workflows.
FHI-aims (Fritz Haber Institute ab initio molecular simulations) is a leading all-electron electronic structure code based on numeric atom-centered orbitals (NAO).
Explore all tools that specialize in simulate molecular dynamics. This domain focus ensures FHI-aims delivers optimized results for this specific requirement.
Explore all tools that specialize in dft calculations. This domain focus ensures FHI-aims delivers optimized results for this specific requirement.
Uses a hierarchical set of basis functions that allows for systematic convergence from fast to 'gold-standard' accuracy.
Native support for the Eigenvalue Solvers for Petaflop-Applications library.
Explicitly simulates both core and valence electrons without pseudopotentials.
Includes ZORA and scalar-relativistic treatments as standard.
Efficient implementation of GW and MP2 for excited states.
Direct output formatting for NOMAD and ASE-compatible JSON.
Integrated linear response and finite-difference methods for phonons.
Obtain license from MS1P e.V. (academic or commercial).
Download source code via secure repository access.
Configure build environment with Fortran compiler (ifort/gfortran) and MPI libraries.
Link with math libraries like Intel MKL, ELPA, or LibXC.
Compile using CMake or manual Makefile for specific HPC architectures.
Set up species data paths in environment variables.
Create geometry.in file defining atomic positions.
Configure control.in for basis set choice (light, tight, or really_tight).
Launch simulation using mpirun or srun on cluster resources.
Parse aims.out using ASE or custom Python scripts for downstream AI training.
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"Highly regarded in the scientific community for uncompromising accuracy and excellent scaling on supercomputers, though with a steep learning curve for non-specialists."
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