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近年来，机器学习正在改变材料研发方式。模型可以从已有材料的组成、结构和性能数据中学习规律，再预测未知材料的性质，从而加快候选材料筛选。但材料数据并不像理想教材那样均匀分布：高性能材料更容易被报道，失败实验和低性能材料往往被忽略；同时，实验和计算成本又使可用数据长期处于“小样本”状态。对于AI模型来说，问题不只是“数据够不够多”，还包括“数据分布是否合理”。如果训练集只集中在少数高性能区域，模型可能会误以为许多未知材料都值得期待；如果只做随机均匀采样，又可能漏掉真正决定性能边界的关键区域。

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