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Introduction

The julia package ACEfriction.jl facilitates simulation and machine learning of configuration-dependent friction tensor models from data. The models are based on an equivariant Atomic Cluster Expansion (ACE) and, as such, are computationally highly efficient and size transferable. The underlying framework of model construction is described in detail in [Sachs et al., (2024)](@ref ACEfriction-paper).

For a quick start, we recommend reading the Installation Instructions and the Overview section, followed by the Workflow Examples. Detailed documentation of front-end-facing functions can be found in the Function Manual.

[References](@id ACEfriction-paper)

If you are using ACEfriction.jl in your work, please cite the following article:

  • Sachs, M., Stark, W. G., Maurer, R. J., & Ortner, C. (2024). Equivariant Representation of Configuration-Dependent Friction Tensors in Langevin Heatbaths. [arxiv]