# Human-AI Co-Creation Approach to Find Forever Chemicals Replacements

arXiv: https://arxiv.org/abs/2304.05389 · HTML: https://mlsanity.com/paper/2304.05389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 7 of 7)

- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Deep Generative Models with Learnable Knowledge Constraints](https://mlsanity.com/paper/1806.09764/) (2018)
- [Generative Models for Automatic Chemical Design](https://mlsanity.com/paper/1907.01632/) (2019)
- [Accelerating Material Design with the Generative Toolkit for Scientific Discovery](https://mlsanity.com/paper/2207.03928/) (2022)
- [Constrained Generative Adversarial Networks for Interactive Image Generation](https://mlsanity.com/paper/1904.02526/) (2019)
- [A unified view of generative models for networks: models, methods, opportunities, and challenges](https://mlsanity.com/paper/1411.4070/) (2014)

## Abstract (arXiv)

Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replacements for the ``forever chemicals''-- chemicals that enable our modern lives, but are harmful to the environment and the human health. Our approach combines AI capabilities with the domain-specific tacit knowledge of subject matter experts to accelerate the material discovery. Our co-creation process starts with the interaction between the subject matter experts and a generative model that can generate new molecule designs. In this position paper, we discuss our hypothesis that these subject matter experts can benefit from a more iterative interaction with the generative model, asking for smaller samples and ``guiding'' the exploration of the discovery space with their knowledge.