Particle-mesh based calculations of long-range interactions in PyTorch
☆83Aug 3, 2026Updated 3 months ago
Alternatives and similar repositories for torch-pme
Users that are interested in torch-pme are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Particle-mesh based calculations of long-range interactions in JAX☆32Oct 20, 2026Updated 2 weeks ago
- Compute neighbor lists for atomistic systems☆89Oct 22, 2026Updated last week
- ☆63Sep 16, 2026Updated last month
- Self-describing sparse tensor data format for atomistic machine learning and beyond☆110Oct 27, 2026Updated last week
- A python package for the prediction of chemical shieldings of organic solids and beyond.☆23Sep 30, 2026Updated last month
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆84Updated this week
- ☆61Updated this week
- electrostatic model sandbox based on the MACE MLIP architecture☆40Oct 24, 2026Updated last week
- ☆142May 3, 2026Updated 6 months ago
- ALCHEMI Toolkit-Ops is a collection of optimized batch kernels to accelerate computational chemistry and material science workflows.☆251Updated this week
- ☆28Oct 26, 2026Updated last week
- ☆13Jun 29, 2025Updated last year
- Enhanced sampling in the representation space of atomistic neural networks.☆17Oct 26, 2026Updated last week
- i-PI: a universal force engine☆322Updated this week
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆108Jan 28, 2026Updated 9 months ago
- ML Performance and Extrapolation Guide (reference deployment https://ml-peg.stfc.ac.uk)☆72Updated this week
- train and use graph-based ML models of potential energy surfaces☆131Sep 22, 2026Updated last month
- An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow.☆18Oct 19, 2025Updated last year
- Zero Shot Molecular Generation via Similarity Kernels☆31Aug 30, 2026Updated 2 months ago
- Computing representations for atomistic machine learning☆83Oct 1, 2026Updated last month
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆109Jul 2, 2026Updated 4 months ago
- Training Neural Network potentials through customizable routines in JAX.☆74Oct 23, 2026Updated last week
- Molecular dimers☆11Jul 14, 2025Updated last year
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- An automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials☆23Jun 6, 2026Updated 4 months ago
- Reciprocal Space Attention (RSA) for Learning Long-Range Interactions☆17Nov 7, 2025Updated 11 months ago
- PyTorch operations for use in creating machine learning interatomic potentials☆22Sep 15, 2026Updated last month
- Auto-differentiated descriptors using Enzyme☆12Apr 2, 2025Updated last year
- A flexible and performant framework for training machine learning potentials.☆39Oct 26, 2026Updated last week
- ML potentials via transfer learning☆31Updated this week
- jax/flax training infrastructure☆16Oct 2, 2026Updated last month
- Implementation of the Euclidean fast attention (EFA) algorithm☆106Jan 7, 2026Updated 9 months ago
- Universal machine-learning models for advanced atomistic simulations☆250Oct 26, 2026Updated last week
- Serverless GPU API endpoints on Runpod - Get Bonus Credits • AdSkip the infrastructure headaches. Auto-scaling, pay-as-you-go, no-ops approach lets you focus on innovating your application.
- Input files for ASE-MC manuscript☆33Jul 30, 2026Updated 3 months ago
- Code for automated fitting of machine learned interatomic potentials.☆165Oct 27, 2026Updated last week
- A python library for calculating materials properties from the PES☆157Updated this week
- OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product.☆172Oct 25, 2026Updated last week
- Pymatgen Core☆30Updated this week
- Build neural networks for machine learning force fields with JAX☆138Jun 2, 2025Updated last year
- Some tutorial-style examples for validating machine-learned interatomic potentials☆35Dec 4, 2023Updated 2 years ago