基于非参数贝叶斯增强采样的自由能景观自适应推断与收敛
Adaptive Inference and Convergence of Free Energy Landscapes Using Non-parametric Bayesian Enhanced Sampling
AI总结：
本研究开发整合高斯过程模型与自适应不确定性驱动增强采样方案，以自由能景观迭代估计训练模型，实时计算不确定性作收敛度量，为复杂分子过程提供可推广的增强采样策略。
AI中文摘要：
增强采样技术是分子现象计算机建模与模拟中研究统计稀有事件的核心方法。本研究开发并整合了高斯过程模型与自适应、不确定性驱动的增强采样方案，该框架基于迭代优化的自由能景观估计值训练高斯过程模型，对反应相空间内高不确定性区域进行增量采样，且不确定性在自由能重构过程中实时计算，用作收敛度量。该方法提供了一种可推广策略，可扩展至复杂分子过程。
英文摘要：
Enhanced sampling techniques are central to the study of statistically rare events in the computer modeling and simulation of molecular phenomena. In this work, we report the development and integration of a Gaussian process model, adaptive, uncertainty-driven sampling scheme for enhanced sampling. The framework trains a Gaussian process model on an iteratively improving estimate of the free energy landscape. Regions of high uncertainty within the reaction phase space are increasingly sampled, and the uncertainty is computed on-the-fly during free energy reconstruction, serving as a convergence metric. This approach provides a generalizable strategy that can be extended to complex molecular processes.
