Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate pools, aims to address this objective. Existing biased and unbiased MD-simulation methods, however, are prone to miss either rare events or extrapolative regions—areas of the configurational space where unreliable predictions are made. This work demonstrates that MD, when biased by the MLIP’s energy uncertainty, simultaneously captures extrapolative regions and rare events, which is crucial for developing uniformly accurate MLIPs. Furthermore, exploiting automatic differentiation, we enhance bias-forces-driven MD with the concept of bias stress. We employ calibrated gradient-based uncertainties to yield MLIPs with similar or, sometimes, better accuracy than ensemble-based methods at a lower computational cost. Finally, we apply uncertainty-biased MD to alanine dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD.

有效地创建一个简洁但全面的数据集来训练机器学习原子间势（MLIP）是一个尚未充分探索的问题。主动学习使用有偏或无偏的分子动力学 (MD) 来生成候选池，旨在实现这一目标。然而，现有的有偏差和无偏差 MD 模拟方法很容易错过罕见事件或外推区域（配置空间中做出不可靠预测的区域）。这项工作表明，当受到 MLIP 能量不确定性的影响时，MD 可以同时捕获外推区域和罕见事件，这对于开发一致准确的 MLIP 至关重要。此外，利用自动微分，我们通过偏置压力的概念增强了偏置力驱动的MD。我们采用基于梯度的校准不确定性来产生 MLIP，其精度与基于集成的方法相似，有时甚至更好，且计算成本较低。最后，我们将不确定性偏差 MD 应用于丙氨酸二肽和 MIL-53(Al)，生成比用传统 MD 训练的模型更准确地表示两个构型空间的 MLIP。