Active Learning for Transition State Calculation

报告题目：Active Learning for Transition State Calculation

报告人：周翔（香港城市大学）

时间：2021-12-16 星期四 14:00-15:00 （15:00-17:00 自由讨论）

地点：C5-206

报告摘要：

The transition state calculation is a grand challenge for computational inverse energy function such as potential energy surface (PES). The traditional methods need to evaluate the gradient of the energy function at a very large number of locations. To reduce the number of expensive computations of the true gradients, we propose an active learning framework consisting of a statistical surrogate model, Gaussian process regression (GPR) for the energy function, and a single-walker dynamics method, gentle accent dynamics (GAD), for saddle search. TS is detected by the GAD applied to the GPR surrogate for the gradient vector and the Hessian matrix of the original model. The active learning is our key ingredient for efficiency improvements, which sequentially designs the most informative locations and takes evaluations of the original model at these locations to train GPR. We formulate this active learning task as the optimal experimental design problem and propose a very efficient sample-based sub-optimal criterion to construct the optimal locations. We show that the new method significantly decreases the required number of energy/ force evaluations of the original model.

报告人简介：

周翔教授本科毕业于北京大学数学科学学院，博士毕业于普林斯顿大学。曾先后在普林斯顿大学和布朗大学做过研究助理（RA），于2012年入职香港城市大学数学系，他主要的研究领域包括稀有事件，随机模型中的计算方法的研究以及机器学习算法。主要研究兴趣包括：非线性随机动力学体系中的transition，随机模拟，鞍点的计算以及高维的非凸能量景观的探索等等。