摘要
Uncertainty quantification(UQ)is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated.Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability,it is essential that practitioners pay attention to the issues concerned.Here aUQstudy is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters,which affect key quantities of interest,including material properties and binding free energy predictions in drug discovery and personalized medicine.Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes,the sensitivity of the input parameters is ranked.Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed.This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters.
基金
funding support from(i)the UK EPSRC for the UK High-End Computing Consortium(EP/R029598/1)
the Software Environment for Actionable&VVUQ-evaluated Exascale Applications(SEAVEA)grant(EP/W007762/1)
the UK Consortium on Mesoscale Engineering Sciences(UKCOMES grant no.EP/L00030X/1)
the Computational Biomedicine at the Exascale(CompBioMedX)grant(EP/X019276/1)
(ii)the UK MRC Medical Bioinformatics project(grant no.MR/L016311/1)
(iii)the European Commission for EU H2020 CompBioMed2 Center of Excellence(grant no.823712)
EU H2020 EXDCI-2 project(grant no.800957)
We made use of SuperMUC-NG at Leibniz Supercomputing Center under project COVID-19-SNG1,and the ARCHER2 UK National Supercomputing Service under the SEAVEA grant(EP/W007762/1).
