让模型预测超出训练数据范围的材料与分子性质，提升极端值预测能力。
Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
- 采用可迁移的归纳方法，利用输入输出间的类比关系进行推断。
- 材料和分子的异常值识别召回率分别提升3倍和2.5倍。
- 适用于高通量材料与分子设计，尤其适合探索未知性能边界。
高性能材料与分子的发现依赖于识别超出已知分布范围的极端性质。因此，将预测能力外推至训练数据之外的分布（即OoD）至关重要。本文目标是仅基于固体或分子图的化学组成及其性质值，训练出能零样本外推至更高范围的预测模型。提出一种可迁移的OoD性质预测方法，在材料和分子任务中均显著提升性能：材料和分子的真阳性率（TPR）分别提高3倍和2.5倍，精确率分别提升2倍和1.5倍。该方法利用训练集与测试集间输入-目标的类比关系，实现对训练目标范围之外的泛化，可推广至其他材料与分子任务。
原文摘要 · Abstract (English)
Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution. Therefore, the ability to extrapolate to out-of-distribution (OOD) property values is critical for both solid-state materials and molecular design. Our objective is to train predictor models that extrapolate zero-shot to higher ranges than in the training data, given the chemical compositions of solids or molecular graphs and their property values. We propose using a transductive approach to OOD property prediction, achieving improvements in prediction accuracy. In particular, the True Positive Rate (TPR) of OOD classification of materials and molecules improved by 3x and 2.5x, respectively, and precision improved by 2x and 1.5x compared to non-transductive baselines. Our method leverages analogical input-target relations in the training and test sets, enabling generalization beyond the training target support, and can be applied to any other material and molecular tasks.
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