MACE foundation models (MP, OMAT, mh-1)
☆318Aug 26, 2026Updated 2 months ago
Alternatives and similar repositories for mace-foundations
Users that are interested in mace-foundations are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆1,403Updated this week
- GRACE models and gracemaker (as implemented in TensorPotential package)☆106Oct 17, 2026Updated 2 weeks ago
- A python library for calculating materials properties from the PES☆157Updated this week
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆277Updated this week
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆109Jul 2, 2026Updated 3 months ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- Code for automated fitting of machine learned interatomic potentials.☆164Updated this week
- electrostatic model sandbox based on the MACE MLIP architecture☆40Oct 24, 2026Updated last week
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆258Updated this week
- ML Performance and Extrapolation Guide (reference deployment https://ml-peg.stfc.ac.uk)☆72Updated this week
- Universal machine-learning models for advanced atomistic simulations☆250Updated this week
- ORB forcefield models from Orbital Materials☆625Sep 26, 2026Updated last month
- Equivariant machine learning interatomic potentials in JAX.☆114Updated this week
- MACE-OFF23 models☆80Jan 29, 2025Updated last year
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆413Sep 15, 2026Updated last month
- End-to-end encrypted email - Proton Mail • AdSpecial offer: 40% Off Yearly / 80% Off First Month. All Proton services are open source and independently audited for security.
- Torch-native, batchable, atomistic simulations.☆505Updated this week
- MatterSim: A deep learning atomistic model across elements, temperatures and pressures.☆608Updated this week
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆108Jan 28, 2026Updated 9 months ago
- Tools for machine learnt interatomic potentials☆51Updated this week
- Collection of tutorials to use the MACE machine learning force field.☆78Sep 8, 2026Updated last month
- NequIP is a code for building E(3)-equivariant interatomic potentials☆971Oct 19, 2026Updated last week
- Graph deep learning library for materials☆579Updated this week
- FAIR Chemistry's library of machine learning methods for chemistry☆2,338Updated this week
- DistMLIP: A Distributed Inference Library for Fast, Large Scale Atomistic Simulation☆99Sep 20, 2025Updated last year
- Deploy open-source AI quickly and easily - Special Bonus Offer • AdRunpod Hub is built for open source. One-click deployment and autoscaling endpoints without provisioning your own infrastructure.
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆84Updated this week
- pytorch implementation of dftd2 & dftd3 (not actively maintained)☆109Oct 21, 2026Updated last week
- Compute neighbor lists for atomistic systems☆88Oct 22, 2026Updated last week
- train and use graph-based ML models of potential energy surfaces☆131Sep 22, 2026Updated last month
- atomate2 is a library of computational materials science workflows☆358Updated this week
- Tensor Atomic/Edge Cluster Expansion☆76Updated this week
- [ICML'26] Phonon fine-tuning (PFT) and [NeurIPS'25 AI4Mat] Nequix: Training a foundation model for materials on a budget☆80Apr 5, 2026Updated 6 months ago
- Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093☆14Jul 8, 2025Updated last year
- ☆61Updated this week
- End-to-end encrypted email - Proton Mail • AdSpecial offer: 40% Off Yearly / 80% Off First Month. All Proton services are open source and independently audited for security.
- Equitrain: A Unified Framework for Training and Fine-tuning Machine Learning Interatomic Potentials☆41Oct 15, 2026Updated 2 weeks ago
- ☆115Sep 4, 2026Updated last month
- ☆142May 3, 2026Updated 5 months ago
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆84Jun 29, 2026Updated 4 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆467Oct 26, 2026Updated last week
- A foundational potential energy dataset for materials☆61Updated this week
- Heat-conductivity benchmark test for foundational machine-learning potentials☆31Jun 28, 2026Updated 4 months ago