审视材料科学中主动学习流程的设计陷阱与优化策略。
A Critical Examination of Active Learning Workflows in Materials Science
- 分析代理模型、采样策略等关键设计对性能的影响
- 揭示常见误区并提出可操作的改进方法
- 适合从事材料机器学习的研究者参考
主动学习（AL）在材料科学中发挥着关键作用，广泛应用于构建机器学习原子间势能和自驱动实验室的运行。尽管应用广泛，其可靠性和有效性依赖于很少被系统检验的隐含设计假设。本文批判性评估了材料科学中部署的主动学习工作流，研究了代理模型、采样策略、不确定性量化及评估指标等关键设计选择如何影响性能。通过识别常见陷阱并讨论实际缓解策略，为研究人员提供了高效设计、评估和解释主动学习工作流的指导。
原文摘要 · Abstract (English)
Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.
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