# A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

arXiv: https://arxiv.org/abs/2304.07445 · HTML: https://mlsanity.com/paper/2304.07445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Optimizing Molecules using Efficient Queries from Property Evaluations](https://mlsanity.com/paper/2011.01921/) (2020)
- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT](https://mlsanity.com/paper/2005.13669/) (2020)

## Abstract (arXiv)

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate.