O
Olli-Pekka Koistinen
Researcher at Aalto University
Publications - 8
Citations - 332
Olli-Pekka Koistinen is an academic researcher from Aalto University. The author has contributed to research in topics: Gaussian process & Degrees of freedom (statistics). The author has an hindex of 7, co-authored 8 publications receiving 232 citations. Previous affiliations of Olli-Pekka Koistinen include Helsinki Institute for Information Technology & University of Iceland.
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Nudged elastic band calculations accelerated with Gaussian process regression.
Olli-Pekka Koistinen,Freyja B. Dagbjartsdóttir,Vilhjálmur Ásgeirsson,Aki Vehtari,Hannes Jónsson +4 more
TL;DR: In this paper, the Hessian matrix at the initial and final state minima can be used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence.
Journal ArticleDOI
Nudged elastic band calculations accelerated with Gaussian process regression
Olli-Pekka Koistinen,Freyja B. Dagbjartsdóttir,Vilhjálmur Ásgeirsson,Aki Vehtari,Hannes Jónsson +4 more
TL;DR: The number of evaluations of the Hessian matrix at the initial and final state minima can be carried out beforehand and used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence.
Journal ArticleDOI
Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression Based on Inverse Interatomic Distances.
Olli-Pekka Koistinen,Olli-Pekka Koistinen,Vilhjálmur Ásgeirsson,Aki Vehtari,Hannes Jónsson,Hannes Jónsson +5 more
TL;DR: A significant improvement to the Gaussian process regression approach is obtained by basing the difference measure between two atomic configurations in the covariance function on the inverted inter-atomic distances and by adding a new early stopping criterion for the path relaxation phase.
Journal ArticleDOI
Minimum energy path calculations with Gaussian process regression
TL;DR: In this paper, Gaussian process regression is used to reduce the number of energy evaluations needed to find minimum energy paths of atomic rearrangements by using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian Process iteration.
Journal ArticleDOI
Minimum energy path calculations with Gaussian process regression
TL;DR: In this paper, Gaussian process regression is used to reduce the number of energy evaluations needed to find minimum energy paths of atomic rearrangements by using results of previous calculations to construct an approximate energy surface and then converge to the minimum energy path on that surface in each Gaussian Process iteration.