Error correction in multi-fidelity molecular dynamics simulations using functional uncertainty quantification

Abstract We use functional, Frechet, derivatives to quantify how thermodynamic outputs of a molecular dynamics (MD) simulation depend on the potential used to compute atomic interactions. Our approach quantifies the sensitivity of the quantities of interest with respect to the input functions as opposed to its parameters as is done in typical uncertainty quantification methods. We show that the functional sensitivity of the average potential energy and pressure in isothermal, isochoric MD simulations using Lennard–Jones two-body interactions can be used to accurately predict those properties for other interatomic potentials (with different functional forms) without re-running the simulations. This is demonstrated under three different thermodynamic conditions, namely a crystal at room temperature, a liquid at ambient pressure, and a high pressure liquid. The method provides accurate predictions as long as the change in potential can be reasonably described to first order and does not significantly affect the region in phase space explored by the simulation. The functional uncertainty quantification approach can be used to estimate the uncertainties associated with constitutive models used in the simulation and to correct predictions if a more accurate representation becomes available.

[1]  C. Chipot,et al.  Cooperative Recruitment of Amphotericin B Mediated by a Cyclodextrin Dimer , 2014 .

[2]  Peter A. Kollman,et al.  FREE ENERGY CALCULATIONS : APPLICATIONS TO CHEMICAL AND BIOCHEMICAL PHENOMENA , 1993 .

[3]  Simon Hanna,et al.  Use of thermodynamic integration to calculate the hydration free energies of n-alkanes , 2002 .

[4]  Christophe Chipot,et al.  Good practices in free-energy calculations. , 2010, The journal of physical chemistry. B.

[5]  Manuel Aldegunde,et al.  Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory , 2016, J. Comput. Phys..

[6]  James Andrew McCammon,et al.  Thermodynamic integration to predict host-guest binding affinities , 2012, Journal of Computer-Aided Molecular Design.

[7]  Richard D. Hornung,et al.  Adaptive sampling in hierarchical simulation , 2007 .

[8]  A. Strachan,et al.  Defect level distributions and atomic relaxations induced by charge trapping in amorphous silica , 2012 .

[9]  Sophia Lefantzi,et al.  DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. , 2011 .

[10]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[11]  Kipton Barros,et al.  Distributed Database Kriging for Adaptive Sampling (D2KAS) , 2015, Comput. Phys. Commun..

[12]  R. Jones,et al.  Uncertainty quantification in MD simulations of concentration driven ionic flow through a silica nanopore. II. Uncertain potential parameters. , 2013, The Journal of chemical physics.

[13]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[14]  A. Stukowski Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool , 2009 .

[15]  A. Hunter,et al.  The role of partial mediated slip during quasi-static deformation of 3D nanocrystalline metals , 2015 .

[16]  Enrique López Droguett,et al.  Bayesian Methodology for Model Uncertainty Using Model Performance Data , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[17]  George E. Karniadakis,et al.  Quantification of sampling uncertainty for molecular dynamics simulation: Time-dependent diffusion coefficient in simple fluids , 2015, J. Comput. Phys..

[18]  Sankaran Mahadevan,et al.  Functional derivatives for uncertainty quantification and error estimation and reduction via optimal high-fidelity simulations , 2013 .

[19]  Christophe Chipot,et al.  Comprar Free Energy Calculations · Theory and Applications in Chemistry and Biology | Chipot, Christophe | 9783540736172 | Springer , 2007 .

[20]  M. Karplus,et al.  Molecular dynamics simulations in biology , 1990, Nature.

[21]  Costas Papadimitriou,et al.  Data driven, predictive molecular dynamics for nanoscale flow simulations under uncertainty. , 2013, The journal of physical chemistry. B.

[22]  Simon R. Phillpot,et al.  Uncertainty Quantification in Multiscale Simulation of Materials: A Prospective , 2013 .

[23]  Andy J. Keane,et al.  A Derivative Based Surrogate Model for Approximating and Optimizing the Output of an Expensive Computer Simulation , 2004, J. Glob. Optim..

[24]  Ramana V. Grandhi,et al.  A Bayesian statistical method for quantifying model form uncertainty and two model combination methods , 2014, Reliab. Eng. Syst. Saf..

[25]  B. Roux,et al.  Determination of membrane-insertion free energies by molecular dynamics simulations. , 2012, Biophysical journal.

[26]  Jaroslaw Knap,et al.  A call to arms for task parallelism in multi‐scale materials modeling , 2011 .

[27]  P. A. Bash,et al.  Free energy calculations by computer simulation. , 1987, Science.

[28]  O. Knio,et al.  Uncertainty quantification in MD simulations of concentration driven ionic flow through a silica nanopore. I. Sensitivity to physical parameters of the pore. , 2013, The Journal of chemical physics.

[29]  Kipton Barros,et al.  Spatial adaptive sampling in multiscale simulation , 2014, Comput. Phys. Commun..

[30]  Berend Smit,et al.  Understanding Molecular Simulation , 2001 .

[31]  Jeremy E. Oakley,et al.  When Is a Model Good Enough? Deriving the Expected Value of Model Improvement via Specifying Internal Model Discrepancies , 2014, SIAM/ASA J. Uncertain. Quantification.

[32]  D. Frenkel Free-energy calculations , 1991 .

[33]  A. Strachan,et al.  Multiscale contact mechanics model for RF–MEMS switches with quantified uncertainties , 2013 .

[34]  J. Sethna,et al.  Bayesian error estimation in density-functional theory. , 2005, Physical review letters.

[35]  J. Tinsley Oden,et al.  Selection, calibration, and validation of coarse-grained models of atomistic systems , 2015 .

[36]  A. D. Kirshenbaum,et al.  THE DENSITY OF LIQUID COPPER FROM ITS MELTING POINT (1356°K.) TO 2500°K. AND AN ESTIMATE OF ITS CRITICAL CONSTANTS1,2 , 1962 .

[37]  Sankaran Mahadevan,et al.  Model uncertainty and Bayesian updating in reliability-based inspection , 2000 .

[38]  Paul N. Patrone,et al.  Uncertainty quantification in molecular dynamics studies of the glass transition temperature , 2016 .

[39]  Michael McLennan,et al.  PUQ: A code for non-intrusive uncertainty propagation in computer simulations , 2015, Comput. Phys. Commun..

[40]  Alejandro Strachan,et al.  Uncertainty propagation in a multiscale model of nanocrystalline plasticity , 2011, Reliab. Eng. Syst. Saf..

[41]  Ali Mosleh,et al.  Integrated treatment of model and parameter uncertainties through a Bayesian approach , 2013 .

[42]  F. RIZZI,et al.  Uncertainty Quantification in MD Simulations. Part I: Forward Propagation , 2012, Multiscale Model. Simul..

[43]  Ramana V. Grandhi,et al.  A Bayesian approach for quantification of model uncertainty , 2010, Reliab. Eng. Syst. Saf..

[44]  Costas Papadimitriou,et al.  Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models , 2015, J. Comput. Phys..

[45]  A. O'Hagan,et al.  Predicting the output from a complex computer code when fast approximations are available , 2000 .

[46]  Khachik Sargsyan,et al.  Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters , 2012, Multiscale Model. Simul..