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.

翻译：生成模型是材料发现领域中的强大工具。我们正在设计一个软件框架，支持人工智能和领域专家的人工智能-人类协作过程，加速寻找“永久化学品”的替代品——这些化学品虽然改善了我们的现代生活，但对环境和人类健康有害。我们的方法结合了人工智能能力和专业主题领域内的暗默知识，以加速材料发现。我们的协作过程始于专题领域专家与可生成新分子设计的生成模型之间的交互。在这篇观点论文中，我们讨论了我们的假设，即这些专题领域专家可以通过与生成模型的更迭交互方式受益，要求更小的样本，并通过他们的知识“引导”发现空间的探索。