facebookresearch/fairchem

FAIR Chemistry's library of machine learning methods for chemistry

What it solves

fairchem provides a centralized repository for materials science and quantum chemistry, offering high-performance machine-learned interatomic potentials (MLIPs) to replace slow, traditional Density Functional Theory (DFT) calculations. It enables researchers to perform atomistic modeling, molecular dynamics, and structural relaxations across a wide range of chemical domains with significantly higher speed and accuracy.

How it works

The project implements the UMA (Universal Materials Architecture) models, which are pretrained models that can be used as calculators for the Atomic Simulation Environment (ASE). By selecting a specific task name (e.g., oc20 for catalysis or omat for inorganic materials), users can leverage these models to predict potential energy and forces for various systems, including catalysts, inorganic crystals, and polymers.

Who it’s for

It is designed for researchers and scientists in materials science, quantum chemistry, and computational chemistry who need to perform large-scale atomistic simulations and molecular dynamics.

Highlights

  • Universal Model Support: Supports a wide array of tasks including (electro)catalysis, inorganic materials, molecules, polymers, and molecular crystals.
  • High Performance: Features a "turbo mode" for molecular dynamics and supports seamless multi-node, multi-GPU inference via Ray.
  • Integration: Fully integrated with the ASE (Atomic Simulation Environment) via the FAIRChemCalculator.
  • Scalability: Compatible with LAMMPs for large-scale dynamics simulations.

Related

  • Project
    ztxz16/fastllm

    FastLLM is a C++‑based, PyTorch‑free inference engine for large language models. It supports dense and MoE models, hybrid GPU/CPU/NUMA/disk deployment, multi‑GPU tensor parallelism, speculative decoding (MTP/DSpark/DFlash2), many quantization formats, and offers a Python CLI, web UI, and OpenAI‑compatible API server. Install via `pip install ftllm`, run a model with `ftllm run`, or launch a service with `ftllm server`. Extensive docs cover model‑specific guides, performance tuning, and community support.

  • Project
    IBM/materials

    IBM/materials is a genuine open‑source suite of large‑scale foundation models for chemistry and materials science. It offers pre‑trained uni‑modal models (SMILES, SELFIES, graphs, 3‑D structures) and multimodal fusion tools, all accessible via a unified Python wrapper (FM4M‑Kit) or a Hugging Face web UI. The repo includes installation instructions, example notebooks, and links to the hosted models on Hugging Face.

  • Project
    lynote-ai/ai-image-detector

    AI Image Detector is an open‑source Python tool that estimates how likely an image was generated by AI. It ships with several ready‑made back‑ends (UnivFD, Sentry, Nonescape, hybrid ensembles) and lets you run a single command (`aidetect detect …`) on a file or folder, outputting a probability and label. Optional extras give a Gradio web UI or a FastAPI server. The repo includes benchmarking scripts, threshold‑calibration, and clear guidance on which backend to pick. It’s a lightweight, install‑once‑run‑anywhere utility for AI‑image detection, with documented limitations (no universal guarantee, no region‑level localization).

  • Project
    ACEsuit/mace

    MACE is an open‑source PyTorch library for equivariant graph‑neural‑network interatomic potentials. It provides fast training/evaluation, multi‑GPU and Apple‑Silicon support, optional CUDA acceleration, and a catalog of pre‑trained foundation models for materials and organic chemistry. Install via `pip install mace-torch`, train with the `mace_run_train` CLI (or YAML configs), and evaluate with `mace_eval_configs`. Documentation, Colab tutorials, and Weights & Biases integration are included. The project is stable on the v0.3 line while a major v1.0 rewrite is in progress.

  • Project
    yakhyo/uniface

    UniFace is a Python library that unifies 15 face‑analysis tasks—detection, landmarks, 3‑D mesh, parsing, matting, gaze, head pose, demographics, emotion, quality, anti‑spoofing, recognition, anonymisation, and FAISS‑based similarity—into a single, easy‑to‑install package (CPU, Apple Silicon, or CUDA). It provides a consistent API, auto‑downloads verified model weights, and includes extensive docs, notebooks, and a community Discord.