Title: Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization Show Chinese title

Title: 评估前沿：主动学习、模型准确性与多目标材料发现和优化

Abstract: Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of \emph{Pareto shell-scope error} to help judge the suitability of a model for proposing material candidates. Further, through synthetic cases and a thermoelectric dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for acquisition function design. Show Chinese abstract

Abstract: 通过迭代机器学习指导的候选材料提案可以大大加速新材料的发现---主动学习。 然而，标准的\emph{全局作用域错误}模型质量指标并不能预测发现性能，可能会产生误导。 我们引入了\emph{帕累托壳范围误差}的概念，以帮助判断模型在提出材料候选物方面的适用性。 此外，通过合成案例和一个热电数据集，我们探讨了获取函数保真度与主动学习性能之间的关系。 结果表明，有新的诊断工具以及获取函数设计的新见解。