@article{ stecher_free_2014,
  title = {Free {Energy} {Surface} {Reconstruction} from {Umbrella} {Samples} {Using} {Gaussian} {Process} {Regression}},
  volume = {10},
  issn = {1549-9618},
  url = {http://dx.doi.org/10.1021/ct500438v},
  doi = {10.1021/ct500438v},
  abstract = {We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org.},
  number = {9},
  urldate = {2015-04-07TZ},
  journal = {Journal of Chemical Theory and Computation},
  author = {Stecher, Thomas and Bernstein, Noam and Csányi, Gábor},
  month = {September},
  year = {2014},
  note = {00002},
  pages = {4079--4097}
}

{"_id":"CJzz7yanbJAPrExzj","bibbaseid":"stecher-bernstein-csnyi-freeenergysurfacereconstructionfromumbrellasamplesusinggaussianprocessregression-2014","downloads":0,"creationDate":"2015-09-03T07:18:47.698Z","title":"Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression","author_short":["Stecher, T.","Bernstein, N.","Csányi, G."],"year":2014,"bibtype":"article","biburl":"http://bibbase.org/zotero/fred.qi","bibdata":{"abstract":"We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org.","author":["Stecher, Thomas","Bernstein, Noam","Csányi, Gábor"],"author_short":["Stecher, T.","Bernstein, N.","Csányi, G."],"bibtex":"@article{ stecher_free_2014,\n title = {Free {Energy} {Surface} {Reconstruction} from {Umbrella} {Samples} {Using} {Gaussian} {Process} {Regression}},\n volume = {10},\n issn = {1549-9618},\n url = {http://dx.doi.org/10.1021/ct500438v},\n doi = {10.1021/ct500438v},\n abstract = {We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org.},\n number = {9},\n urldate = {2015-04-07TZ},\n journal = {Journal of Chemical Theory and Computation},\n author = {Stecher, Thomas and Bernstein, Noam and Csányi, Gábor},\n month = {September},\n year = {2014},\n note = {00002},\n pages = {4079--4097}\n}","bibtype":"article","doi":"10.1021/ct500438v","id":"stecher_free_2014","issn":"1549-9618","journal":"Journal of Chemical Theory and Computation","key":"stecher_free_2014","month":"September","note":"00002","number":"9","pages":"4079--4097","title":"Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression","type":"article","url":"http://dx.doi.org/10.1021/ct500438v","urldate":"2015-04-07TZ","volume":"10","year":"2014","bibbaseid":"stecher-bernstein-csnyi-freeenergysurfacereconstructionfromumbrellasamplesusinggaussianprocessregression-2014","role":"author","urls":{"Paper":"http://dx.doi.org/10.1021/ct500438v"},"downloads":0},"search_terms":["free","energy","surface","reconstruction","umbrella","samples","using","gaussian","process","regression","stecher","bernstein","csányi"],"keywords":[],"authorIDs":[],"dataSources":["y8en6y5RHukfeLuPH"]}