Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIPs). It offers a growing set of evaluation methods alongside powerful visualization and comparison tools.
☆96Jan 28, 2026Updated last month
Alternatives and similar repositories for mlipx
Users that are interested in mlipx are comparing it to the libraries listed below
Sorting:
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆90Updated this week
- Compute neighbor lists for atomistic systems☆74Updated this week
- Tools for machine learnt interatomic potentials☆44Feb 21, 2026Updated last week
- Display and Edit Molecules (https://zndraw.icp.uni-stuttgart.de)☆49Feb 16, 2026Updated last week
- train and use graph-based ML models of potential energy surfaces☆121Feb 20, 2026Updated last week
- ⚛ download and manipulate atomistic datasets☆48Nov 25, 2025Updated 3 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆84Dec 17, 2025Updated 2 months ago
- Code for automated fitting of machine learned interatomic potentials.☆137Updated this week
- Machine Learned Interatomic Potential Tools☆24Updated this week
- Quick Uncertainty and Entropy via STructural Similarity☆56Updated this week
- CUDA implementations of MACE models☆23Aug 19, 2025Updated 6 months ago
- ORB forcefield models from Orbital Materials☆546Feb 17, 2026Updated last week
- ☆119Feb 10, 2026Updated 2 weeks ago
- A python library for calculating materials properties from the PES☆131Updated this week
- Auto-differentiated descriptors using Enzyme☆12Apr 2, 2025Updated 10 months ago
- pytorch implementation of dftd2 & dftd3 (not actively maintained)☆91Jan 13, 2026Updated last month
- Torch-native, batchable, atomistic simulations.☆416Feb 21, 2026Updated last week
- ☆35Updated this week
- Library for Crystal Symmetry in Rust☆69Updated this week
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆59Updated this week
- JAX implementation of the NequIP neural network interatomic potential☆16Updated this week
- Collection of tutorials to use the MACE machine learning force field.☆53Jan 22, 2026Updated last month
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆212Feb 14, 2026Updated 2 weeks ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆76Jan 29, 2026Updated last month
- [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential☆59Sep 26, 2025Updated 5 months ago
- atomate2 is a library of computational materials science workflows☆283Updated this week
- Self-describing sparse tensor data format for atomistic machine learning and beyond☆94Updated this week
- Heat-conductivity benchmark test for foundational machine-learning potentials☆30Jan 29, 2026Updated last month
- Reproduction of CGCNN for predicting material properties☆23Feb 2, 2026Updated 3 weeks ago
- Zero Shot Molecular Generation via Similarity Kernels☆28Aug 27, 2025Updated 6 months ago
- Equivariant machine learning interatomic potentials in JAX.☆87Feb 10, 2026Updated 2 weeks ago
- Alchemical machine learning interatomic potentials☆34Nov 8, 2024Updated last year
- Adds Orb Model functionality to LAMMPS via Python wrapping☆15Apr 1, 2025Updated 10 months ago
- quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.☆247Updated this week
- python workflow toolkit☆43Dec 23, 2025Updated 2 months ago
- Computing representations for atomistic machine learning☆78Feb 4, 2026Updated 3 weeks ago
- MACE foundation models (MP, OMAT, mh-1)☆203Updated this week
- This repository contains the source code for Bayesian Learned Interatomic Potentials (BLIP)☆31Aug 20, 2025Updated 6 months ago
- DFT dataset and machine learning models for high entropy alloys☆22Apr 19, 2024Updated last year