We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The interpolation is defined at the sample distribution level, and the neural network potential is optimized to match the corresponding equilibrium potential at every intermediate time-step. Once the interpolating potentials and samples are well-aligned, the free-energy difference can be estimated using (neural) thermodynamic integration. To target molecular systems, we simultaneously couple Lennard-Jones and electrostatic interactions and model the rigid-body rotation of molecules. We report accurate results for several benchmark systems: a Lennard-Jones particle in a Lennard-Jones fluid, as well as the insertion of both water and methane solutes in a water solvent at atomistic resolution using a simple three-body neural-network potential.

翻译：本文提出了一种利用热力学积分结合神经网络势函数计算自由能差的方法，该神经网络势函数可在两个目标哈密顿量之间进行插值。插值操作在样本分布层面定义，并通过优化神经网络势函数使其在每个中间时间步长匹配相应的平衡势函数。当插值势函数与样本充分对齐后，即可利用（神经）热力学积分估算自由能差。针对分子体系，我们同时耦合了Lennard-Jones相互作用与静电相互作用，并对分子的刚体转动进行建模。我们在多个基准体系上获得了精确的计算结果：包括Lennard-Jones流体中的Lennard-Jones粒子，以及使用简单的三体神经网络势函数在原子分辨率下计算水与甲烷溶质在水溶剂中的插入过程。