The adaptive buffered force QM/MM method in the CP2K and AMBER software packages

The implementation and validation of the adaptive buffered force (AdBF) quantum-mechanics/molecular-mechanics (QM/MM) method in two popular packages, CP2K and AMBER are presented. The implementations build on the existing QM/MM functionality in each code, extending it to allow for redefinition of the QM and MM regions during the simulation and reducing QM-MM interface errors by discarding forces near the boundary according to the buffered force-mixing approach. New adaptive thermostats, needed by force-mixing methods, are also implemented. Different variants of the method are benchmarked by simulating the structure of bulk water, water autoprotolysis in the presence of zinc and dimethyl-phosphate hydrolysis using various semiempirical Hamiltonians and density functional theory as the QM model. It is shown that with suitable parameters, based on force convergence tests, the AdBF QM/MM scheme can provide an accurate approximation of the structure in the dynamical QM region matching the corresponding fully QM simulations, as well as reproducing the correct energetics in all cases. Adaptive unbuffered force-mixing and adaptive conventional QM/MM methods also provide reasonable results for some systems, but are more likely to suffer from instabilities and inaccuracies.N.B. acknowledges funding for this work by the Office of Naval Research through the Naval Research Laboratory's basic research program, and computer time at the AFRL DoD Supercomputing Resource Center through the DoD High Performance Computing Modernization Program (subproject NRLDC04253428). B.L. was supported by EPSRC (grant no. EP/G036136/1) and the Scottish Funding Council. G.C. and B.L. acknowledge support form EPSRC under grant no. EP/J01298X/1. R.C.W. and A.W.G. acknowledge financial support by the National Institutes of Health (R01 GM100934), A.W.G. acknowledges financial support by the Department of Energy (DE-AC36-99GO-10337). This work was partially supported by National Science Foundation (grant no. OCI-1148358) and used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant no. ACI-1053575. Computer time was provided by the San Diego Supercomputer Center through XSEDE award TG-CHE130010.This is the author accepted version of the article. The final published version is available from Wiley at http://onlinelibrary.wiley.com/doi/10.1002/jcc.23839/full