Library for efficient training and application of Machine Learning Interatomic Potentials (MLIP)
☆84Jan 7, 2026Updated 2 months ago
Alternatives and similar repositories for mlip
Users that are interested in mlip are comparing it to the libraries listed below
Sorting:
- ☆11Sep 16, 2024Updated last year
- An ASE-friendly implementation of the amorphous-to-crystalline (a2c) workflow.☆18Oct 19, 2025Updated 5 months ago
- CUDA implementations of MACE models☆23Aug 19, 2025Updated 7 months ago
- AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks☆38Jun 4, 2025Updated 9 months ago
- This repository contains the source code for Bayesian Learned Interatomic Potentials (BLIP)☆31Aug 20, 2025Updated 7 months ago
- tools for graph-based machine-learning potentials in jax☆26Apr 9, 2024Updated last year
- JAX implementation of the NequIP neural network interatomic potential☆17Feb 24, 2026Updated 3 weeks ago
- ☆13Feb 11, 2025Updated last year
- More efficient and faster version of pyscal☆28Mar 2, 2026Updated 2 weeks ago
- ☆16Feb 17, 2025Updated last year
- train and use graph-based ML models of potential energy surfaces☆122Mar 9, 2026Updated last week
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆61Updated this week
- [NeurIPS'25 AI4Mat] Nequix: Training a foundation model for materials on a budget and [arXiv'26] Phonon fine-tuning (PFT)☆70Updated this week
- ☆24Nov 1, 2024Updated last year
- ☆22May 7, 2025Updated 10 months ago
- ☆37Feb 22, 2026Updated 3 weeks ago
- Reproduction of CGCNN for predicting material properties☆24Mar 10, 2026Updated last week
- Computing representations for atomistic machine learning☆79Updated this week
- ☆44Updated this week
- Equitrain: A Unified Framework for Training and Fine-tuning Machine Learning Interatomic Potentials☆11Jan 28, 2026Updated last month
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆96Jan 28, 2026Updated last month
- Torch-native, batchable, atomistic simulations.☆432Updated this week
- MESS: Modern Electronic Structure Simulations☆43Sep 26, 2025Updated 5 months ago
- Heat-conductivity benchmark test for foundational machine-learning potentials☆30Jan 29, 2026Updated last month
- ☆25Aug 20, 2025Updated 7 months ago
- A collection of simulation recipes for the atomic-scale modeling of materials and molecules☆45Updated this week
- Universal interatomic potentials for advanced materials modeling☆174Mar 13, 2026Updated last week
- Alchemical machine learning interatomic potentials☆34Nov 8, 2024Updated last year
- Cross-platform Optimizer for ML Interatomic Potentials☆24Aug 31, 2025Updated 6 months ago
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆92Feb 23, 2026Updated 3 weeks ago
- 🪐 The Sebulba architecture to scale reinforcement learning on Cloud TPUs in JAX☆61Oct 23, 2023Updated 2 years ago
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Oct 30, 2024Updated last year
- DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning (Nature Computational Science, 2026)☆35Feb 10, 2026Updated last month
- Local Environment-based Atomic Features☆13Dec 19, 2024Updated last year
- A universal ML model to predict molecular dynamics trajectories with long time steps☆42Mar 1, 2026Updated 3 weeks ago
- Compute neighbor lists for atomistic systems☆74Mar 10, 2026Updated last week
- Quick Uncertainty and Entropy via STructural Similarity☆57Feb 23, 2026Updated 3 weeks ago
- COMPASS: Combinatorial Optimization with Policy Adaptation using Latent Space Search☆42Jun 21, 2024Updated last year
- Some tutorial-style examples for validating machine-learned interatomic potentials☆34Dec 4, 2023Updated 2 years ago