用主动学习优先级队列，让生成模型更高效发现优质碳捕集分子。
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
- 通过主动学习筛选候选分子，动态优化生成模型的探索方向。
- 在1000个新分子中，高质量候选数从281提升至604个。
- 适合需要高效探索复杂化学空间的研究者使用。
生成式AI为科学中的逆向设计问题带来机遇与挑战。虽然生成工具能自主扩展和优化搜索空间，但可能在未充分调优前浪费资源于低质量区域。本文提出一种结合生成建模与主动学习的队列优先级算法，应用于分布式工作流以探索复杂设计空间。结果表明，引入主动学习模型对候选分子进行优先排序，可避免生成模型盲目探索无效结构，并防止模型性能退化。针对现有用于发现新型碳捕集分子结构的生成式工作流，该方法显著提升了高质量候选物的数量：无主动学习时平均产出281个高性能候选，而采用主动学习优先级后，平均可达604个。
原文摘要 · Abstract (English)
Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space autonomously, but do so at the cost of exploring low-quality regions until sufficiently fine tuned. Here, we propose a queue prioritization algorithm that combines generative modeling and active learning in the context of a distributed workflow for exploring complex design spaces. We find that incorporating an active learning model to prioritize top design candidates can prevent a generative AI workflow from expending resources on nonsensical candidates and halt potential generative model decay. For an existing generative AI workflow for discovering novel molecular structure candidates for carbon capture, our active learning approach significantly increases the number of high-quality candidates identified by the generative model. We find that, out of 1000 novel candidates, our workflow without active learning can generate an average of 281 high-performing candidates, while our proposed prioritization with active learning can generate an average 604 high-performing candidates.
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