Generating a data set that is representative of the accessible configuration
space of a molecular system is crucial for the robustness of machine learned
interatomic potentials (MLIP). However, the complexity of molecular systems,
characterized by intricate potential energy surfaces (PESs) with numerous local
minima and energy barriers, presents a significant challenge. Traditional
methods of data generation, such as random sampling or exhaustive exploration,
are either intractable or may not capture rare, but highly informative
configurations. In this study, we propose a method that leverages uncertainty
as the collective variable (CV) to guide the acquisition of chemically-relevant
data points, focusing on regions of the configuration space where ML model
predictions are most uncertain. This approach employs a Gaussian Mixture
Model-based uncertainty metric from a single model as the CV for biased
molecular dynamics simulations. The effectiveness of our approach in overcoming
energy barriers and exploring unseen energy minima, thereby enhancing the data
set in an active learning framework, is demonstrated on the alanine dipeptide
benchmark system.

为机器学习的原子间势提供了能够代表分子系统可访问配置空间的数据集的生成方法，该方法利用不确定性作为集体变量以引导获取与化学相关的数据点，重点关注机器学习模型预测最不确定性的配置空间区域，并在丙氨酰二肽基准系统上验证了该方法在克服能量壁垒和探索未知能量最小值方面的有效性。

基于不确定性的集体变量增强采样的稳健分子数据集

Enhanced sampling of robust molecular datasets with uncertainty-based  collective variables

Many-body descriptors are widely used to represent atomic environments in the
construction of machine learned interatomic potentials and more broadly for
fitting, classification and embedding tasks on atomic structures. It was
generally believed that 3-body descriptors uniquely specify the environment of
an atom, up to a rotation and permutation of like atoms. We produce several
counterexamples to this belief, with the consequence that any classifier,
regression or embedding model for atom-centred properties that uses 3 (or
4)-body features will incorrectly give identical results for different
configurations. Writing global properties (such as total energies) as a sum of
many atom-centred contributions mitigates, but does not eliminate, the impact
of this fundamental deficiency -- explaining the success of current
"machine-learning" force fields. We anticipate the issues that will arise as
the desired accuracy increases, and suggest potential solutions.

本文指出传统上人们认为的三体描述符不能唯一地指定一个原子环境，并对此提出了几个反例，解释了当前 “机器学习” 力场的成功。作者预测了随着所需精度的提高而产生的问题，并提出潜在的解决方案。