Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model
Abstract
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. In conclusion, 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.
- Authors:
-
- Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1526444
- Alternate Identifier(s):
- OSTI ID: 1507189
- Grant/Contract Number:
- AC02-76SF00515
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Volume: 122; Journal Issue: 15; Journal ID: ISSN 0031-9007
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; 97 MATHEMATICS AND COMPUTING
Citation Formats
Garrido Torres, José A., Jennings, Paul C., Hansen, Martin H., Boes, Jacob R., and Bligaard, Thomas. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model. United States: N. p., 2019.
Web. doi:10.1103/physrevlett.122.156001.
Garrido Torres, José A., Jennings, Paul C., Hansen, Martin H., Boes, Jacob R., & Bligaard, Thomas. Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model. United States. https://doi.org/10.1103/physrevlett.122.156001
Garrido Torres, José A., Jennings, Paul C., Hansen, Martin H., Boes, Jacob R., and Bligaard, Thomas. Mon .
"Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model". United States. https://doi.org/10.1103/physrevlett.122.156001. https://www.osti.gov/servlets/purl/1526444.
@article{osti_1526444,
title = {Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model},
author = {Garrido Torres, José A. and Jennings, Paul C. and Hansen, Martin H. and Boes, Jacob R. and Bligaard, Thomas},
abstractNote = {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. In conclusion, 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.},
doi = {10.1103/physrevlett.122.156001},
journal = {Physical Review Letters},
number = 15,
volume = 122,
place = {United States},
year = {Mon Apr 15 00:00:00 EDT 2019},
month = {Mon Apr 15 00:00:00 EDT 2019}
}
Web of Science
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