Nimbus Therapeutics
Pioneering Structure-Based Drug Discovery via Advanced Computational Physics and ML.
Updated 13d ago
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Small molecule design
Binding affinity prediction
Protein-ligand docking
FHI-aims
4.8
Paid Highly regarded in the scientific community for uncompromising accuracy and excellent scaling on supercomputers, though with a steep learning curve for non-specialists.
All-electron, full-potential electronic structure theory for high-fidelity molecular and materials discovery.
DevelopmentPaid pricingAPI availableUpdated 2026-03-17
Best fits
Density Functional Theory (DFT) calculationsMachine Learning dataset generation
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About FHI-aims
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.
Core Capabilities
FHI-aims (Fritz Haber Institute ab initio molecular simulations) is a leading all-electron electronic structure code based on numeric atom-centered orbitals (NAO).
