gp-acceleration

GP Acceleration

Surrogate energy surfaces via Gaussian process regression for 10x fewer force evaluations

The approach

Gaussian process (GP) regression builds a surrogate energy surface on the fly, interpolating between the few force evaluations already computed. The GP predicts energies and forces at new configurations, and its uncertainty estimates indicate where to sample next. This reduces force evaluation counts by roughly 10x compared to the standard dimer method (Goswami et al. 2025).

The implementation lives in the eOn saddle point search code. It works in simple Cartesian coordinates rather than requiring specialized internal coordinates, and interoperates with standard electronic structure codes.

Adaptive pruning

As the GP collects data during a search, the cost of updating the model grows cubically with dataset size. On large problems, the surrogate itself becomes the bottleneck. We solve this with farthest-point sampling guided by the Earth Mover’s Distance, a metric that measures structural similarity between molecular configurations in a permutation-invariant way. This adaptive pruning halves wall time while preserving accuracy (Goswami and Jónsson 2025). The work earned a cover feature in ChemPhysChem.

Code

  • eOn – Co-maintainer; GP saddle search integration

References

Goswami, Rohit, and Hannes Jónsson. 2025. “Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches.” Chemphyschem (Cover Feature), November. https://doi.org/10.1002/cphc.202500730.
Goswami, Rohit, Maxim Masterov, Satish Kamath, Alejandro Pena-Torres, and Hannes Jónsson. 2025. “Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions.” Journal of Chemical Theory and Computation, July. https://doi.org/10.1021/acs.jctc.5c00866.

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