通过广义分层贝叶斯推理进行自由能计算的不确定性量化
Uncertainty Quantification for Free Energy Calculations by Generalized Hierarchical Bayesian Inference
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中文总结 AI 辅助
研究自由能计算中不确定性量化问题，开发广义分层高斯过程框架，应用于肽 - 脂质膜相互作用的相关模拟，能在多样条件下跟踪重建误差，使不确定性估计适应数据信息。
中文摘要 AI 辅助
自由能计算常用于研究无偏分子动力学无法触及的分子过程，但其效用取决于能否知晓预测何时及多大程度可信。不确定性估计对区分自由能分布的真实物理特征与有限模拟数据或采样不足产生的伪像至关重要。高斯过程已成为重建自由能分布及预测不确定性的有力框架，但现有实现通常基于固定超参数和观测噪声，使预测不确定性无法适应模拟数据的信息内容。在此，我们开发了一个广义分层高斯过程框架来考虑这些被忽视的不确定性来源。将其应用于肽 - 脂质膜相互作用的伞形采样和扩展拉格朗日元动力学表明，所得不确定性估计在广泛的采样和数据条件下跟踪重建误差。
英文摘要
Free energy calculations are routinely used to study molecular processes inaccessible to unbiased molecular dynamics, but their utility ultimately depends on knowing when and how much their predictions can be trusted. Uncertainty estimation is therefore essential for distinguishing genuine physical features of a free energy profile from artifacts arising from limited simulation data or inadequate sampling. Gaussian processes have emerged as a powerful framework for reconstructing free energy profiles together with predictive uncertainties. However, existing implementations typically condition on fixed hyperparameters and observation noise, preventing predictive uncertainties from adapting to the information content of the simulation data. Here, we develop a generalized hierarchical Gaussian process framework that accounts for these neglected sources of uncertainty. Applications to umbrella sampling and extended Lagrangian metadynamics of peptide-lipid membrane interactions demonstrate that the resulting uncertainty estimates track reconstruction errors across a wide range of sampling and data conditions.
