这篇论文给出了一个很实用的思路：用多个预训练模型做教师，主动拒绝不可靠的结构，只用少量高精度标签就能生成大量高质量数据，对材料模拟的同学很有参考价值。
该论文提出自适应多教师路由(ATR)方法，仅用0.2%的候选结构真实r$^2$SCAN标签，即可生成289万条可追溯的r$^2$SCAN级伪标签用于预训练。在MP-r$^2$SCAN基准上，基于ATR数据集训练的轻量级CHGNet模型持续优于基线和非路由对照组。有限温度分子动力学实验表明，ATR在多个材料系统中提高了动力学鲁棒性，避免了模拟中的灾难性结构坍缩。
Active rejection enables reliable generalization of universal machine-learning interatomic potentials
Universal machine learning interatomic potentials (uMLIPs) bridge quantum-mechanical accuracy and large-scale molecular dynamics, but the cost of high-accuracy calculations such as r$^2$SCAN limits training to datasets that remain small relative to the open materials space. Strong average benchmark performance also does not guarantee reliable energy--force predictions for every structure. We propose Adaptive Multi-Teacher Routing (ATR), which reformulates high-fidelity data construction as a structure-wise decision problem under uncertainty. Using a small set of real r$^2$SCAN labels, ATR calibrates multiple pretrained uMLIP teachers and combines structural descriptors, teacher identity, and inter-teacher disagreement to estimate the reliability of each structure--teacher pair. It selects high-confidence predictions for pseudo-label generation and rejects structures for which no teacher is sufficiently reliable. With real r$^2$SCAN labels for only 0.2\% of candidate structures, ATR distils 2.89 million traceable r$^2$SCAN-level pseudo-labels for pretraining. On held-out r$^2$SCAN structures and the MP-r$^2$SCAN benchmark, a lightweight CHGNet trained on the ATR-generated dataset consistently outperforms the baseline and non-routed controls. Finite-temperature molecular dynamics further shows that ATR improves dynamical robustness across multiple material systems, maintaining stable trajectories where baseline simulations undergo catastrophic structural collapse. These results establish active rejection as an effective mechanism for converting multiple pretrained uMLIPs into a scalable and reliable data-construction system for high-fidelity uMLIPs.