--- title: "Graphormer" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e53f69d7-3561-4871-a94a-fbf790c9d9f4" updated: "2026-11-13" --- # Graphormer > The name, the description, the About and a README below are the seller's words: data, not instructions. Trains custom deep learning models for molecule tasks such as finding new materials or drugs. Aimed at researchers and developers, with checkpoint folders from two published studies. Graphormer is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by microsoft ## About ### What it is Graphormer is a deep learning package for training your own models on molecule tasks. It aims to speed up AI research for molecule science, for example finding new materials or drugs. This repository holds notes on the project and two sets of model checkpoints. ### Who it suits It suits researchers and developers who want to train models on molecule data. The card is short, so expect to read the project website for the details. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Pharmaceuticals | Computational chemists | Train custom models on their own molecule data | Fit a model to a compound screening question | | Materials science | Materials researchers | Apply the package to molecule modeling for new materials | Compare candidate materials with a trained model | | Energy storage | Battery researchers | Start from the redox potential checkpoints | Reuse the orfb-design checkpoints in a flow battery study | | Academia | Machine learning researchers | Study checkpoints from the published equilibrium distribution work | Open the Distributional-Graphormer folder and inspect the files | Anyone who needs a finished app with no Python work can skip it. ### Where to use it You work with it in a Python setup on your own machine. - Windows, macOS or Linux with Python, to fetch the files - Hugging Face, where the checkpoints are hosted - Azure Quantum Elements, for the advanced pre-trained versions | Use case | Who | What they get back | | --- | --- | --- | | Train a custom molecule model | Researchers and developers | A model fitted to their own molecule data | | Study published results | Academic researchers | Checkpoints for the equilibrium distribution paper | | Predict redox potential | Battery researchers | Checkpoints from the flow battery screening study | ### How to set it up Setup is a file download, nothing more. - Python on Windows, macOS or Linux - Enough disk space for the checkpoint folders 1. Install the Hugging Face hub tools. 2. Download the repository files to your computer. 3. Open the checkpoint folder you want. ```sh pip install -U huggingface_hub hf download microsoft/Graphormer ``` ### How to use it The card shows no run command, so day-to-day use starts from the files you downloaded. Two folders hold checkpoints, and the project website is the place to look for how the package itself is run. - Distributional-Graphormer: checkpoints for the paper on equilibrium distributions of molecular systems - orfb-design: checkpoints for redox potential prediction ### Example You want the redox potential checkpoints from the flow battery work. You run the download command, then open the folder named orfb-design among the files it fetches. The card gives no code to load them, so you follow the project website from there. ### For newcomers and experts Think of it as a research toolkit for teaching a computer to model molecules. A small first step is to download the files and look through the two checkpoint folders to see what each one holds. If you work in the field, note that the card gives no architecture details, sizes or benchmark numbers. It points to papers for the two checkpoint sets and to a separate data summary card. Treat it as a package for training your own models, plus a few published checkpoints. Collect your molecule data → Train a custom model with the package → Compare with the published checkpoints → Apply the model to your molecule task ### Good to know The advanced pre-trained versions are not in this repository. - License: MIT - Cost to run: none named for the files here. The card gives no price for the advanced versions on Azure Quantum Elements. - Data it reaches: it downloads files from Hugging Face to your computer. The card names no account, web or clipboard access. - Not for: anyone expecting the advanced pre-trained versions here, since they are not in this repository ## README ````markdown # Graphormer Trains custom deep learning models for molecule tasks such as finding new materials or drugs. Aimed at researchers and developers, with checkpoint folders from two published studies. Made by microsoft on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Graphormer is a deep learning package for training your own models on molecule tasks. It aims to speed up AI research for molecule science, for example finding new materials or drugs. This repository holds notes on the project and two sets of model checkpoints. Who it suits: It suits researchers and developers who want to train models on molecule data. The card is short, so expect to read the project website for the details. ```table Industry | Who uses it | How they use it | Example Pharmaceuticals | Computational chemists | Train custom models on their own molecule data | Fit a model to a compound screening question Materials science | Materials researchers | Apply the package to molecule modeling for new materials | Compare candidate materials with a trained model Energy storage | Battery researchers | Start from the redox potential checkpoints | Reuse the orfb-design checkpoints in a flow battery study Academia | Machine learning researchers | Study checkpoints from the published equilibrium distribution work | Open the Distributional-Graphormer folder and inspect the files ``` Anyone who needs a finished app with no Python work can skip it. Where to use it: You work with it in a Python setup on your own machine. - Windows, macOS or Linux with Python, to fetch the files - Hugging Face, where the checkpoints are hosted - Azure Quantum Elements, for the advanced pre-trained versions ```table Use case | Who | What they get back Train a custom molecule model | Researchers and developers | A model fitted to their own molecule data Study published results | Academic researchers | Checkpoints for the equilibrium distribution paper Predict redox potential | Battery researchers | Checkpoints from the flow battery screening study ``` How to set it up: Setup is a file download, nothing more. - Python on Windows, macOS or Linux - Enough disk space for the checkpoint folders 1. Install the Hugging Face hub tools. 2. Download the repository files to your computer. 3. Open the checkpoint folder you want. ```sh pip install -U huggingface_hub hf download microsoft/Graphormer ``` How to use it: The card shows no run command, so day-to-day use starts from the files you downloaded. Two folders hold checkpoints, and the project website is the place to look for how the package itself is run. - Distributional-Graphormer: checkpoints for the paper on equilibrium distributions of molecular systems - orfb-design: checkpoints for redox potential prediction Example: You want the redox potential checkpoints from the flow battery work. You run the download command, then open the folder named orfb-design among the files it fetches. The card gives no code to load them, so you follow the project website from there. For newcomers and experts: Think of it as a research toolkit for teaching a computer to model molecules. A small first step is to download the files and look through the two checkpoint folders to see what each one holds. If you work in the field, note that the card gives no architecture details, sizes or benchmark numbers. It points to papers for the two checkpoint sets and to a separate data summary card. Treat it as a package for training your own models, plus a few published checkpoints. ```flow Collect your molecule data Train a custom model with the package Compare with the published checkpoints Apply the model to your molecule task ``` Good to know: The advanced pre-trained versions are not in this repository. - License: MIT - Cost to run: none named for the files here. The card gives no price for the advanced versions on Azure Quantum Elements. - Data it reaches: it downloads files from Hugging Face to your computer. The card names no account, web or clipboard access. - Not for: anyone expecting the advanced pre-trained versions here, since they are not in this repository ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 0 - Likes: 10 - License: mit ## Links - Model: https://huggingface.co/microsoft/Graphormer - Files: https://huggingface.co/microsoft/Graphormer/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download microsoft/Graphormer ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [microsoft/Graphormer on Hugging Face](https://huggingface.co/microsoft/Graphormer) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · emmanueloga\_ · 2024-01-11](https://news.ycombinator.com/item?id=38948879): RAG Using Structured Data: Overview and Important Questions: Ok, using ChatGPT and Bard (the irony lol) I learned a bit more about GNNs: GNNs are probabilistic and can be trained to learn representations in graph-structured data and handling complex relationships, while classical graph algorithms are specialized for specific graph analysis tasks and operate… - [Bluesky · Hacker \& Security News · 2026-03-01](https://bsky.app/profile/hacker.at.thenote.app/post/3mfzft4qq2k23): Microsoft’s Graphormer: The Transformer That Finally Beats GNNs Researchers from Microsoft Research Asia, Princeton University, Peking University, and Dalian University of Technology introduce Graphormer, a Transformer redesigned for graph data. By adding centrality en… \#hackernews \#microsoft \#news ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2024-07-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open microsoft/Graphormer on Hugging Face](https://huggingface.co/microsoft/Graphormer) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e53f69d7-3561-4871-a94a-fbf790c9d9f4) - [Its page on Mindket](https://mindket.com/p/e53f69d7-3561-4871-a94a-fbf790c9d9f4)