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：生成式模型是人工智能在材料发现领域的有力工具。我们正在设计一套支持人类-AI协作流程的软件框架，以加速寻找“永久化学品”的替代物——这些化学品虽支撑着现代生活，却对环境和人类健康有害。我们的方法结合了人工智能能力与领域专家的隐性专业知识，以加速材料发现。该协作流程始于领域专家与能生成新分子设计的生成式模型之间的互动。在这篇立场论文中，我们提出假设：领域专家可通过与生成式模型进行更迭代的交互获得益处，即要求更小规模的样本，并利用其专业知识“引导”发现空间的探索。