28 dependents
| Package | Description | Downloads/month |
|---|---|---|
| For on-the-fly active learning of interatomic potentials | 12K | |
| MDI engine drivers for LAMMPS — MACE and other ML forcefields via MolSSI Driver ... | 3K | |
| A Python package for processing omol-25 data using MPI. | 2K | |
| Access pre-trained MACE models | 2K | |
| MCP server for MACE machine learning interatomic potentials | 1K | |
| Agentic framework for computational chemistry and materials science workflows | 1K | |
| A VASP-like interface for running ML potential calculations with MACE | 1K | |
| DeePMD-kit plugin for graph neural network models. | 636 | |
| Easy access to our research code | 620 | |
| Batched optimisation algorithms for neural network potential driven molecular dy... | 612 | |
| Intelligent Scientific Agent for Materials Design | 608 | |
| Automated machine-learned Potential Landscape explorer | 594 | |
| QUASAR: Quantum Universal Autonomous System for Atomistic Research | 582 | |
| A Python package for the creation of input files for CP2K, MACE-torch, MatterSim... | 434 | |
| Python package containing a variety of distance functions for crystals, as well ... | 268 | |
| Implementation of the So3krates model in pytorch | 190 | |
| Active Learning framework for atomistic simulations with flexible workflows and ... | 168 | |
| Python software that implements the formulation for evaluating the effects of el... | 156 | |
| Calculates ligand strain of small molecules from their docked poses. | 148 | |
| A Python interface for implementation of crystal structure pre-relaxation and pr... | 137 | |
| 133 | ||
| 113 | ||
| mace model inference package. | 108 | |
| Crystalyse v1.0 - Intelligent Scientific AI Agent for Inorganic Materials Design | 97 | |
| AI Materials Scientist - Analyze atomic surfaces using MLIPs and various microsc... | 79 | |
| MACE IPSuite Plugin | 72 | |
| ZnDraw Extensions | 66 | |
| A Python package for processing omol-25 data using MPI. | 49 |
