Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation, deep evidential regression, and Gaussian mixture models. We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness; MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.

神经网络（NNs）往往对其预测赋予高置信度，即便对于分布外的点也不例外，这使得不确定性量化（UQ）成为一个挑战。当它们被用于模拟材料系统中的原子间势时，这一问题会导致产生非物理结构，从而破坏模拟，或者导致偏颇的统计和动力学，无法反映真实物理。可微分的不确定性量化技术可以发掘新的信息性数据，并驱动活跃学习循环，以实现稳健的势。然而，针对原子模拟存在多种不确定性量化技术，包括新开发的，而对于哪些技术最为有效或适合特定案例，尚无明确的指导方针。在本研究中，我们考察了多种不确定性量化方案，通过活跃学习来提升神经网络原子间势（NNIPs）的稳健性。特别是，我们比较了基于集成的现有方法与采用单一、确定性神经网络的战略：均值-方差估计、深度证据回归和高斯混合模型。我们探索了三个数据集，这些数据集涵盖了从域内插值学习到更具外推性的域外泛化挑战：rMD17、氨气倒转和块状石英玻璃。性能通过多个与模型误差相关的不确定性指标进行衡量。我们的实验表明，在各个指标上，没有任何一种方法能始终优于其他方法。集成方法在泛化和NNIP稳健性方面仍占优势；MVE仅在域内插值中证明有效，而GMM在域外更为出色；尽管证据回归具有潜力，但在任何案例中都不是首选的替代方案。更广泛地说，成本效益较高的单一确定性模型在NNIPs的不确定性量化方面，尚不能始终与集成方法相匹配或超越。