mlsanityData to 14 Oct 2026

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

arXiv 2304.05389 ↗First posted 11 Apr 2023cs.AI cs.HCcited by 0

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

  1. 2022Investigating Explainability of Generative AI for Code through Scenario-based Designcited by 47
  2. 2018GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentationcited by 41
  3. 2018Deep Generative Models with Learnable Knowledge Constraintscited by 23
  4. 2019Generative Models for Automatic Chemical Designcited by 23
  5. 2022Accelerating Material Design with the Generative Toolkit for Scientific Discoverycited by 16
  6. 2019Constrained Generative Adversarial Networks for Interactive Image Generationcited by 10
  7. 2014A unified view of generative models for networks: models, methods, opportunities, and challengescited by 3

Built on by

Not cited yet.