Human-AI Co-Creation Approach to Find Forever Chemicals Replacements
Not on the map. Why?
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.
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
7 of 21 references.
References · 7
- 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
- 2018GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentationcited by 41
- 2018Deep Generative Models with Learnable Knowledge Constraintscited by 23
- 2019Generative Models for Automatic Chemical Designcited by 23
- 2022Accelerating Material Design with the Generative Toolkit for Scientific Discoverycited by 16
- 2019Constrained Generative Adversarial Networks for Interactive Image Generationcited by 10
- 2014A unified view of generative models for networks: models, methods, opportunities, and challengescited by 3
Built on by
Not cited yet.