# AutoOED: Automated Optimal Experiment Design Platform

arXiv: https://arxiv.org/abs/2104.05959 · HTML: https://mlsanity.com/paper/2104.05959/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables](https://mlsanity.com/paper/1910.01688/) (2019)
- [Constrained Bayesian Optimization for Automatic Chemical Design](https://mlsanity.com/paper/1709.05501/) (2017)

## Built on by (most cited first, 3 of 3)

- [EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery](https://mlsanity.com/paper/2605.24018/) (2026)
- [ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization](https://mlsanity.com/paper/2510.03051/) (2025)
- [SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization](https://mlsanity.com/paper/2510.25404/) (2025)

## Abstract (arXiv)

We present AutoOED, an Optimal Experiment Design platform powered with automated machine learning to accelerate the discovery of optimal solutions. The platform solves multi-objective optimization problems in time- and data-efficient manner by automatically guiding the design of experiments to be evaluated. To automate the optimization process, we implement several multi-objective Bayesian optimization algorithms with state-of-the-art performance. AutoOED is open-source and written in Python. The codebase is modular, facilitating extensions and tailoring the code, serving as a testbed for machine learning researchers to easily develop and evaluate their own multi-objective Bayesian optimization algorithms. An intuitive graphical user interface (GUI) is provided to visualize and guide the experiments for users with little or no experience with coding, machine learning, or optimization. Furthermore, a distributed system is integrated to enable parallelized experimental evaluations by independent workers in remote locations. The platform is available at https://autooed.org.