MACE Force Fields — Web Interface¶
Run DFT-accuracy atomistic simulations entirely in your browser. No installation. No command line. No barriers.
Live Demo: mace-lake.vercel.app
What is this?¶
A browser-based interface for MACE (Multi-Atomic Cluster Expansion) machine learning interatomic potentials. Upload a crystal or molecular structure, pick a model, and get publication-quality energies, forces, and trajectories in seconds.
Why it exists and the story behind it¶
I am a freshman college student majoring in computer science. I was lucky to be assigned a research project by Oak Ridge National Laboratory, focusing on the use of MACE.
MACE is a powerful tool for quantum chemistry, but I initially had no idea how to use it. The detailed documentation and Google Colab are not user-friendly, at least for me.
So I decided to act.
I am not an expert in quantum chemistry or machine-learning interatomic potentials, but I'm a front-end developer. I can visualize complex, abstract tools and simplify the command-line interface into a modern software tool accessible to researchers and even students in my research team.
Rowan Scientific, a molecular design and simulation platform for scientists, truly inspires me. They share my vision of making complex scientific tools more accessible to researchers. I would like to include their quote:
"We're starting Rowan because we think that scientific software shouldn't be hard to use."
It is inspiring when I realize they had the exact same vision as I did!
Especially with the age of AI, modern software using AI agents becomes increasingly easy to build, and the quality is amazing. Even someone without a PhD in quantum chemistry can build a research-grade web interface for others to use.
Machine learning interatomic potentials like MACE have reached DFT-level accuracy while running orders of magnitude faster. But using them still requires Python scripting, command-line fluency, and environment setup that shuts out many researchers — especially those with accessibility needs, those in under-resourced labs, or students encountering computational chemistry for the first time.
This project removes that barrier.
Key capabilities¶
| Feature | Details |
|---|---|
| Structure input | Drag-and-drop .xyz, .cif, .poscar, .pdb or pick from a built-in catalog of 14 benchmark structures |
| Foundation models | MACE-MP-0 (89 elements, materials) and MACE-OFF (organic molecules) in small/medium/large |
| Custom models | Upload your own .model file and compare against foundation models |
| Calculations | Single-point energy & forces, geometry optimization (BFGS), molecular dynamics (NVE/NVT/NPT) |
| Visualization | Dual 3D viewers (3Dmol.js + WEAS), interactive Plotly charts, MD trajectory player |
| Sharing | Every result becomes a permanent shareable URL via MACE Link |
| Benchmark | Batch-evaluate multiple models across multiple structures with leaderboard and export |
| Accessibility | Keyboard navigation, ARIA labels, colorblind-safe palette (Paul Tol) |
Quick links¶
- Getting Started — install and run locally in 3 commands
- Calculator Guide — walkthrough of a calculation
- MACE Calculator Parameters — complete parameter reference
- Models — which model to pick for your system
- Validation — how results are verified
- Architecture — system design and data flow
- API Reference — calculation API details
Built by Zicheng Zhao · Northeastern University