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.
Rare Event Kinetics from Adaptive Bias Enhanced Sampling
我们介绍了一种新的增强采样方法命名为OPES洪水计算罕见事件的动力学原子分子动力学模拟。该方法来源于即时概率增强采样（OPES）方法[Invernizzi和Parrinello，JPC Lett. 2020]，其最近被开发用于计算复杂系统的收敛自由能表面。在本文中，我们描述的理论细节的OPES泛技术，并展示了应用程序上的三个系统的日益复杂：障碍交叉在一个二维的双阱势，在气相中的丙氨酸二肽的构象转变，和折叠和展开的chignolin多肽在水环境中。从广泛的测试，我们表明，准确的动力学计算不仅需要的过渡态是无偏置，但沉积的偏压量也不应超过沿所选的集体变量测量的有效势垒高度。在这种情况下，也探讨了从偏置次优序参量计算速率的可能性。此外，我们描述了从有限的计算工作量获得准确的结果的最佳参数组合的选择。
