Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically chosen collective variables (CVs). We propose using differentiable simulations (DiffSim) for the discovery and enhanced sampling of chemical transformations without a need to resort to preselected CVs, using only a distance metric. Reaction path discovery and estimation of the biasing potential that enhances the sampling are merged into a single end-to-end problem that is solved by path-integral optimization. This is achieved by introducing multiple improvements over standard DiffSim such as partial backpropagation and graph mini-batching making DiffSim training stable and efficient. The potential of DiffSim is demonstrated in the successful discovery of transition paths for the Muller-Brown model potential as well as a benchmark chemical system - alanine dipeptide.

翻译：模拟稀有事件（如化学反应中反应物转化为产物）通常需要依赖启发式选择的集体变量（CVs）的增强采样技术。我们提出采用可微模拟（DiffSim）来发现并增强化学转化的采样，无需预先选定集体变量，仅使用距离度量即可。反应路径发现与增强采样的偏置势估计被融合为一个端到端问题，并通过路径积分优化求解。这一目标通过对标准DiffSim进行多项改进（如部分反向传播与图小批次处理）得以实现，从而使DiffSim训练稳定高效。DiffSim的潜力在成功发现Muller-Brown模型势能及基准化学体系——丙氨酸二肽的过渡路径中得到了验证。