用神经网络势能计算溶剂化自由能，精度高且无需手工设计势函数。
Solvation Free Energies from Neural Thermodynamic Integration
- 基于神经网络构建可插值的势能函数，逐时步匹配平衡态分布。
- 在原子分辨率下对水和甲烷溶质插入水相的自由能差计算误差小于0.5 kcal/mol。
- 适用于含非键作用与刚体转动的复杂分子体系，适合做分子模拟研究。
我们提出一种基于神经网络势能的热力学积分方法，用于计算自由能差。该方法通过在样本分布层面定义插值路径，使神经网络势能在每个中间时间步均匹配对应平衡态势能。当插值势能与采样结果充分对齐后，即可通过（神经）热力学积分估计自由能差。为模拟分子体系，我们同时耦合Lennard-Jones和静电相互作用，并建模分子刚体转动。在多个基准系统上取得高精度结果：在原子分辨率下对一个Lennard-Jones粒子嵌入Lennard-Jones流体，以及水和甲烷溶质插入水溶剂的自由能差计算，仅使用简单三体神经网络势能，误差低于0.5 kcal/mol。
原文摘要 · Abstract (English)
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.
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