Active-learning machine-operated molecular dynamics (ALmoMD) is a Python code package designed for the effective training of machine learned interatomic potential (MLIP) through active learning (AL) based on uncertainty estimates. It also facilitates the implementation of molecular dynamics (MD) using trained MLIPs with the uncertainty evaluation.
The primary goal of ALmoMD is to efficiently train the MLIP without posing a challenge to ab initio molecular dynamics, while effectively capturing rare dynamical events, leading to a more reliable MLIP. Additionally, ALmoMD includes the implementation of MD with uncertainty evaluation, providing an indication of its reliability.
ALmoMD utilizes multiple code packages to implement the active learning scheme for MLIP and MLIP-MD, with plans to introduce additional interfaces in the future.
- Atomic Simulation Environment (ASE)
- Machine Learned Interatomic Potential (NequIP, So3krates)
- Density Functional Theory (FHI-aims)
- ab initio Molecular dynamics and anharmonicity evalulation (FHI-vibes)
MIT License
Copyright (c) [2024] [Kisung Kang]
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