通过队列优先级排序引导主动学习工作流实现新型材料发现
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
AI总结：
本研究提出一种结合生成建模与主动学习的队列优先级排序算法，用于分布式材料发现工作流，可避免资源浪费、阻止模型衰减，在碳捕获分子发现任务中使1000个候选物里的高性能候选数从平均281个提升至604个。
摘要：
生成式AI为解决科学领域的逆向设计问题既带来了机遇也带来了风险。生成式工具具备自主扩展和优化搜索空间的能力，但代价是需要探索低质量区域，直到完成充分的微调。本文提出一种队列优先级排序算法，该算法在用于探索复杂设计空间的分布式工作流场景下，结合了生成建模与主动学习技术。我们发现，引入主动学习模型对优质设计候选者进行优先级排序，能够避免生成式AI工作流在无意义的候选对象上耗费资源，同时阻止生成模型出现潜在的性能衰减。针对一个已有的、用于发现碳捕获新型分子结构候选物的生成式AI工作流，我们的主动学习方法显著提升了生成模型识别出的高质量候选物数量。结果显示，在1000个新型候选物中，未采用主动学习的工作流平均可生成281个高性能候选物，而我们提出的结合主动学习的优先级排序方法平均可生成604个高性能候选物。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
