论文标题
使用深层生成模型计算绝对自由能
Computing Absolute Free Energy with Deep Generative Models
论文作者
论文摘要
快速准确评估自由能，从药物设计到材料工程都有广泛的应用。计算绝对自由能特别感兴趣，因为它允许在不使用中间体的情况下评估状态之间的相对稳定性。在这封信中，我们引入了一个通用框架，用于计算状态的绝对自由能。计算的关键步骤是使用本地采样配置的参考状态定义具有可拖动的深层生成模型的定义。该参考状态的绝对自由能按设计为零。然后可以确定感兴趣状态的自由能作为参考的差异。我们将这种方法应用于离散和连续系统，并证明了其有效性。发现Bennett接受率方法比基于工作的近似表达式提供了更准确，更有效的自由能估计。我们预计此处提出的方法是计算自由能差异的宝贵策略。
Fast and accurate evaluation of free energy has broad applications from drug design to material engineering. Computing the absolute free energy is of particular interest since it allows the assessment of the relative stability between states without the use of intermediates. In this letter, we introduce a general framework for calculating the absolute free energy of a state. A key step of the calculation is the definition of a reference state with tractable deep generative models using locally sampled configurations. The absolute free energy of this reference state is zero by design. The free energy for the state of interest can then be determined as the difference from the reference. We applied this approach to both discrete and continuous systems and demonstrated its effectiveness. It was found that the Bennett acceptance ratio method provides more accurate and efficient free energy estimations than approximate expressions based on work. We anticipate the method presented here to be a valuable strategy for computing free energy differences.
