Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany.
Atomistic Simulations, Italian Institute of Technology, Via Morego 30, Genova 16163, Italy.
J Phys Chem Lett. 2021 Oct 7;12(39):9449-9454. doi: 10.1021/acs.jpclett.1c02135. Epub 2021 Sep 23.
We present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of theory from a cheaper reference potential. The convergence is accelerated by a mapping function that increases the overlap between the target and the reference distributions. Building on recent work, we show that this map can be learned with a normalizing flow neural network, without requiring simulations with the expensive target potential but only a small number of single-point calculations, and, crucially, avoiding the systematic error that was found previously. We validate the method by numerically evaluating the free energy difference in a system with a double-well potential and by describing the free energy landscape of a simple chemical reaction in the gas phase.
我们提出了一种方法，将靶向自由能微扰（TFEP）理论扩展到从更便宜的参考势能计算准确量子力学水平的自由能差和自由能面。通过映射函数加速收敛，该函数增加了目标和参考分布之间的重叠。基于最近的工作，我们表明可以使用归一化流神经网络来学习这个映射，而不需要使用昂贵的目标势能进行模拟，只需要进行少量单点计算，并且至关重要的是，避免了之前发现的系统误差。我们通过数值评估双势阱系统中的自由能差以及描述气相中简单化学反应的自由能面来验证该方法。
