Abstract
Advances in recent years have made molecular dynamics (MD) and Monte Carlo (MC) simulations powerful tools in molecular-level research, allowing the prediction of experimental observables in the study of systems such as proteins, membranes, and polymeric materials. However, the quality of any prediction based on molecular dynamics results will strongly depend on the validity of underlying physical assumptions. Unphysical behavior of simulations can have significant influence on the results and reproducibility of these simulations, such as folding of proteins and DNA or properties of lipid bilayers determined by cutoff treatment, dynamics of peptides and polymers affected by the choice of thermostat, or liquid properties depending on the simulation time step. Motivated by such examples, we propose a two-fold approach to increase the robustness of molecular simulations. The first part of this approach involves tests which can be performed by the users of MD programs on their respective systems and setups. We present a number of tests of different complexity, ranging from simple post-processing analysis to more involved tests requiring additional simulations. These tests are shown to significantly increase the reliability of MD simulations by catching a number of common simulation errors violating physical assumptions, such as non-conservative integrators, deviations from the Boltzmann ensemble, and lack of ergodicity between degrees of freedom. To make the usage as easy as possible, we have developed an open-source and platform-independent Python library (https://physical-validation.readthedocs.io) implementing these tests. The second part of the approach involves testing for code correctness. While unphysical behavior can be due to poor or incompatible choices of parameters by the user, it can just as well originate in coding errors within the program. We therefore propose to include physical validation tests in the code-checking mechanism of MD software packages. We have implemented such a validation for the GROMACS software package, ensuring that every major release passes a number of physical sanity checks performed on selected representative systems before shipping. It is, to our knowledge, the first major molecular mechanics software package to run such validation routinely. The tests are, as the rest of the package, open source software, and can be adapted for other software packages.
Highlights
Advances in recent years have made molecular dynamics (MD) simulations a powerful tool in molecular-level research, allowing the prediction of experimental observables in the study of systems such as proteins, drug targets [1, 2], membranes and polymers [3]
We show that these tests can significantly increase the reliability of MD simulations by catching a number of common simulation errors violating physical assumptions, such as non-conservative integrators, deviations from the Boltzmann ensemble, and lack of ergodicity between degrees of freedom
We have reviewed a number of important physical laws that valid molecular simulations must satisfy, and demonstrated how these physical laws can be checked using a number of simple tests, as well as presented an easy-to-use physical validation software suite to assess molecular simulation correctness and improve simulation reproducibility
Summary
Advances in recent years have made molecular dynamics (MD) simulations a powerful tool in molecular-level research, allowing the prediction of experimental observables in the study of systems such as proteins, drug targets [1, 2], membranes and polymers [3]. The sensitivity of the K-S test of the kinetic energy distribution depends on the number of degrees of freedom and the target temperature. As the distribution of configurational quantities like the potential energy U, the volume V (for the isothermal-isobaric (NPT) ensemble) or the number of each species N (for the grand-canonical (μVT) ensemble) are in general not known analytically, testing the likelihood of a trajectory sampling a given ensemble is less straightforward than for the kinetic energy. Most tests presented need relatively simple information, such as the units and the time step used to ensure proper calculation, information on the temperature and pressure control and the number of degrees of freedom to select the right tests, and trajectories of energies, volume and pressure to perform the validation. Section A in S4 Text lists code snippets containing example usages of the different parsers
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