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,例如将反应器转换成化学反应中的产品,通常要求采用依赖超自然选择的集体变量的强化取样技术。我们提议使用不同的模拟(DiffSim)来发现和强化化学变异的取样,而无需仅使用一个距离度量即可进行预选的化学变异。反应路径发现和估计增强取样的偏差潜力被合并成一个单一端到端的问题,通过路径整体优化解决。这是通过对标准 DiffSim 进行多种改进来实现的,例如部分反射和图形微型比对,使 DiffSim 培训稳定和高效。DiffSim 的潜力表现在成功发现穆勒-布朗模型潜力的过渡路径以及基准化学系统-alanine Diptide。