算法
替代模型
克里金
计算机科学
鞍点
趋同(经济学)
高斯过程
缩放比例
概率逻辑
高斯分布
统计物理学
人工智能
数学
机器学习
物理
经济增长
量子力学
几何学
经济
作者
José Antonio Garrido Torres,Paul C. Jennings,Martin Hangaard Hansen,Jacob R. Boes,Thomas Bligaard
标识
DOI:10.1103/physrevlett.122.156001
摘要
We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. Our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.
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