# LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design

arXiv: https://arxiv.org/abs/2208.14567 · HTML: https://mlsanity.com/paper/2208.14567/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)

## Built on by (most cited first, 9 of 9)

- [Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning](https://mlsanity.com/paper/2305.08279/) (2023)
- [LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis](https://mlsanity.com/paper/2405.20592/) (2024)
- [DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications](https://mlsanity.com/paper/2305.09018/) (2023)
- [Symbolic Intermediaries as a Linguistic-Numerical Interface for LLM-Driven Geometric Reasoning](https://mlsanity.com/paper/2505.17607/) (2025)
- [Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions](https://mlsanity.com/paper/2402.14882/) (2024)
- [Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design](https://mlsanity.com/paper/2410.18358/) (2024)
- [MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation](https://mlsanity.com/paper/2508.09005/) (2025)
- [Creative synthesis of kinematic mechanisms](https://mlsanity.com/paper/2510.03308/) (2025)
- [Discrete Autoregressive Transformer for Generative Mechanism Synthesis](https://mlsanity.com/paper/2606.17409/) (2026)

## Abstract (arXiv)

In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, etc which are typically what most databases include. LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms. The curated paths are provided as a measure for removing biases in the paths generated by mechanisms that enable a more even design space representation. In this paper, we discuss the details of how we can generate such a large dataset and how we can overcome major issues with such scales. To be able to generate such a large dataset we introduce a new operator to generate 1-DOF mechanism topologies, furthermore, we take many steps to speed up slow simulations of mechanisms by vectorizing our simulations and parallelizing our simulator on a large number of threads, which leads to a simulation 800 times faster than the simple simulation algorithm. This is necessary given on average, 1 out of 500 candidates that are generated are valid~(and all must be simulated to determine their validity), which means billions of simulations must be performed for the generation of this dataset. Then we demonstrate the depth of our dataset through a bi-directional chamfer distance-based shape retrieval study where we show how our dataset can be used directly to find mechanisms that can trace paths very close to desired target paths.