Enhanced sampling of particular degrees of freedom in molecular systems based on adiabatic decoupling and temperature or force scaling

Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

Experimental and ab initio investigations of microscopic properties of laser-shocked Ge-doped ablator

Plastic materials (CH) doped with mid-Z elements are used as ablators in inertial confinement fusion (ICF) capsules and in their surrogates. Hugoniot equation of state (EOS) and electronic properties of CH doped with germanium (at 2.5% and 13% dopant fractions) are investigated experimentally up to 7 Mbar using velocity and reflectivity measurements of shock fronts on the GEKKO laser at Osaka University. Reflectivity and temperature measurements were updated using a quartz standard. Shocked quartz reflectivity was measured at 532 and 1064 nm. Theoretical investigation of shock pressure and reflectivity was then carried out by ab initio simulations using the quantum molecular dynamics (QMD) code abinit and compared with tabulated average atom EOS models. We find that shock states calculated by QMD are in better agreement with experimental data than EOS models because of a more accurate description of ionic structure. We finally discuss electronic properties by comparing reflectivity data to a semiconductor gap closure model and to QMD simulations.

Temperature-Accelerated Sampling and Amplified Collective Motion with Adiabatic Reweighting to Obtain Canonical Distributions and Ensemble Averages

In molecular simulations, accelerated sampling can be achieved efficiently by raising the temperature of a small number of coordinates. For collective coordinates, the temperature-accelerated molecular dynamics method or TAMD has been previously proposed, in which the system is extended by introducing virtual variables that are coupled to these coordinates and simulated at higher temperatures (Maragliano, L.; Vanden-Eijnden, E. Chem. Phys. Lett.2005, 426, 168-175). In such accelerated simulations, steady state or equilibrium distributions may exist but deviate from the canonical Boltzmann one. We show that by assuming adiabatic decoupling between the subsystems simulated at different temperatures, correct canonical distributions and ensemble averages can be obtained through reweighting. The method makes use of the low-dimensional free energy surfaces that are estimated as Gaussian mixture probability densities through maximum likelihood and expectation maximization. Previously, we proposed the amplified collective motion method or ACM. The method employs the coarse-grained elastic network model or ANM to extract collective coordinates for accelerated sampling. Here, we combine the ideas of ACM and of TAMD to develop a general technique that can achieve canonical sampling through reweighting under the adiabatic approximation. To test the validity and accuracy of adiabatic reweighting, first we consider a single n-butane molecule in a canonical stochastic heat bath. Then, we use explicitly solvated alanine dipeptide and GB1 peptide as model systems to demonstrate the proposed approaches. With alanine dipeptide, it is shown that sampling can be accelerated by more than an order of magnitude with TAMD while correct distributions and canonical ensemble averages can be recovered, necessarily through adiabatic reweighting. For the GB1 peptide, the conformational distribution sampled by ACM-TAMD, after adiabatic reweighting, suggested that a normal simulation suffered significantly from insufficient sampling and that the reweighted ACM-TAMD distribution may present significant improvements over the normal simulation in representing the local conformational ensemble around the folded structure of GB1.

New functionalities in the GROMOS biomolecular simulation software

Since the most recent description of the functionalities of the GROMOS software for biomolecular simulation in 2005 many new functions have been implemented. In this article, the new functionalities that involve modified forces in a molecular dynamics (MD) simulation are described: the treatment of electronic polarizability, an implicit surface area and internal volume solvation term to calculate interatomic forces, functions for the GROMOS coarse-grained supramolecular force field, a multiplicative switching function for nonbonded interactions, adiabatic decoupling of a number of degrees of freedom with temperature or force scaling to enhance sampling, and nonequilibrium MD to calculate the dielectric permittivity or viscosity. Examples that illustrate the use of these functionalities are given.