Learning to interpolate molecular potential energy surfaces with confidence: a Bayesian approach
A modified form of Shepard interpolation of ab initio molecular potential energy surfaces is presented. This approach yields significant improvement in accuracy over previous related schemes. Here each Taylor expansion used in the interpolation formula is assigned a confidence volume which controls the relative weight assigned to that expansion. The parameters determining this confidence volume are derived automatically from a simple Bayesian analysis of the interpolation data. As the iterative...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Chemical Physics|
|01_Bettens_Learning_to_interpolate_1999.pdf||529.46 kB||Adobe PDF||Request a copy|
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