Skip to main content
kUPS
kUPS demo

A toolkit for building high-performance molecular simulations on JAX

kUPS provides composable, differentiable primitives — samplers, potentials, and propagators — with hardware acceleration on CPU, GPU, and TPU.

Documentation | Quick Start | Features | Examples

License Python JAX


Installation

Standard Installation
pip install kups
GPU Support
pip install kups[cuda]
Development
git clone https://github.com/cusp-ai-oss/kups.git
cd kUPS
uv sync

Quick Start

Monte Carlo Simulation (GCMC)
cd examples
kups_mcmc_rigid mcmc_rigid.yaml
Molecular Dynamics (Lennard-Jones)
cd examples
kups_md md_lj_argon_nvt.yaml

Features

Simulation Methods

  • Monte Carlo — NVT and GCMC ensembles with translation, rotation, reinsertion, and exchange moves
  • Molecular Dynamics — NVE, NVT, NPT ensembles
  • Geometry Optimization — FIRE and L-BFGS relaxation

Force Fields & Potentials

  • Lennard-Jones potential
  • Coulomb interactions (Ewald summation)
  • Harmonic bonds and angles
  • Morse potential
  • MACE and UMA ML force fields

Core Capabilities

  • Composable — shared propagator interface; methods and potentials snap together freely
  • Batched — run thousands of independent simulations as vectorized computations

Performance & Integration

  • GPU-native — JIT-compiled on CPU, GPU, and TPU with no code changes
  • Differentiable — full automatic differentiation via JAX
  • PyTorch interop — bring PyTorch models into JAX via Tojax

Documentation

Full documentation is available at cusp-ai-oss.github.io/kUPS.

Building docs locally

To build the docs locally run ./docs/scripts/build.sh, which executes and renders all documentation notebooks and generates API pages in markdown.

./docs/scripts/build.sh           # build into site/
./docs/scripts/build.sh --serve   # serve with live updates on http://127.0.0.1:8000

Citation

If you use kUPS in your research, please cite:

@software{kups2026,
  author = {Gao, Nicholas
    and K{\"o}hler, Jonas
    and Hanke, Felix
    and Ramanan, Anita
    and Moubarak, Elias
    and Morrow, Joe
    and de Haan, Pim
    and Openshaw, Hannah
    and Welling, Max
    and CuspAI Team},
  title = {kUPS - a universal particle simulation toolkit},
  year = {2026},
  url = {https://github.com/cusp-ai-oss/kups}
}

License

Apache License 2.0 — see LICENSE.

Metadata

Release files for kups 1.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for kups 1.0.6
File Size Uploaded
kups-1.0.6.tar.gz 357.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for kups 1.0.6
File Interpreter ABI Platform
kups-1.0.6-py3-none-any.whl Python 3 none any Details

Total release size: 833.4 kB

Release files / kups-1.0.6.tar.gz

Download URL kups-1.0.6.tar.gz
Size 357.3 kB
Tags Source
SHA-256 checksum
How to use checksums
659017a89e7bc7796d7fa60b1cb4880ba8a193aecfb5a9b447f25f8850706fbe
BLAKE2b-256 checksum
How to use checksums
d8d0f9f1036dd46b00be76523e0487ee928b8b6579845815e83af4cafb97f434
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 17, 2026.

Transparency log

Release files / kups-1.0.6-py3-none-any.whl

Download URL kups-1.0.6-py3-none-any.whl
Size 476.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
9fa06acd208d9aa95458637d0a91857ce8a413cac5c3467b3099f8e63ae5dfa2
BLAKE2b-256 checksum
How to use checksums
f888fd6c67ab16f0c699ecb684c27cf8be085e9676fb8c8121324967cf49bc02
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 17, 2026.

Transparency log

Release history Release notifications | RSS feed

1.0.8

2 release files

1.0.7

2 release files

This release

1.0.6 This release

2 release files

1.0.5

2 release files

1.0.4

2 release files

1.0.3

2 release files

1.0.2

2 release files

1.0.1

2 release files

1.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page