Details

Autor(en) / Beteiligte

• Garrido Torres, José A
• Jennings, Paul C
• Hansen, Martin H
• Boes, Jacob R
• Bligaard, Thomas

Titel

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model

Ist Teil von

• Physical review letters, 2019-04, Vol.122 (15), p.156001-156001, Article 156001

Ort / Verlag

United States: American Physical Society

Erscheinungsjahr

2019

Link zum Volltext

Quelle

American Physical Society Journals

Beschreibungen/Notizen

• 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.