发表机构
Fudan University; Shanghai Innovation Institute; Shanghai Artificial Intelligence Laboratory; Suzhou Laboratory（复旦大学; 上海创新研究院; 上海人工智能实验室; 苏州实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对通用机器学习原子间势训练数据集受限问题，提出自适应多教师路由方法，通过校准预训练教师、估计结构-教师对可靠性来生成伪标签，提升模型性能和动力学鲁棒性，有效构建高保真uMLIPs数据系统。
AI 中文摘要
通用机器学习原子间势（uMLIPs）兼顾量子力学精度和大规模分子动力学，但高精度计算成本限制了训练数据集规模。我们提出自适应多教师路由（ATR），将高保真数据构建重新表述为不确定性下的结构决策问题。利用少量真实标签校准多个预训练uMLIP教师，结合结构描述符等估计结构-教师对的可靠性，选择高置信度预测生成伪标签，拒绝不可靠结构。仅用0.2%候选结构的真实标签，ATR就能为预训练提取大量可追溯伪标签。在测试集和基准测试中，基于ATR生成数据集训练的模型表现出色，有限温度分子动力学表明其提升了动力学鲁棒性。结果表明主动拒绝是将多个预训练uMLIPs转换为高保真uMLIPs可扩展可靠数据构建系统的有效机制。
英文摘要
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.
