Flaviu Cipcigan, Vlad Sokhan, Jason Crain, Glenn Martyna
Transcription
Flaviu Cipcigan, Vlad Sokhan, Jason Crain, Glenn Martyna
Electronically coarse-grained methods for accurate and efficient molecular modelling Flaviu Cipcigan, Vlad Sokhan, Jason Crain, Glenn Martyna flaviu.cipcigan@ed.ac.uk Introduction Electronically coarse grained water model The ability to design materials with desired properties is a major challenge with both societal and economic impact. The way computer aided design revolutionised manufacturing, atomic and molecular simulation will revolutionise the creation of advanced materials. Computers can explore structures faster than experiment, study materials in extreme conditions and guide synthesis. The properties of the model (whose features are illustrated fig 1) are defined entirely in terms of isolated molecules, unbiased towards any thermodynamic state. Yet, it predicts the following to within 1–2% of experiment: However, current tools are not always adequate for this job. Classical force-fields, based on fitting model parameters to condensed phase data transfer poorly to states outside the parametrisation regime. On the other hand, accurate ab initio methods scale unfavourably with system size. In a collaboration between University of Edinburgh, National Physical Laboratory and IBM T.J. Watson Research Center, we are developing a novel strategy for atomistic modelling, where the electronic degrees of freedom are coarse grained to a single quantum harmonic oscillator (called a Quantum Drude Oscillator, or QDO). The dynamics of interacting QDOs are simulated efficiently via two–temperature path integral molecular dynamics. In this approach, all many-body forces are on equal footing, dramatically reducing required empirical input. Furthermore, parametrisation needs no condensed phase data, resulting in models that are extremely transferable between environments. The ultimate goal is to create a framework to support the design of technologically important materials, such as synthetic biomolecules and electrolytes. A molecular model for water is the first step towards this goal. The model has also revealed new insights into the physics of water. 800 700 T (K) 600 320 500 280 400 240 0.96 0.98 1.0 300 0 0.2 0.4 0.6 0.8 1.0 density (kg m-3) Figure 2: The liquid–vapour coexistence curve of the model matches experiment. The molecular dipole moment reveals an remnant of the liquid–vapour transition in supercritical water. The supercritical fluid is thought to be a single phase. However, we found that abrupt changes in the dipole moment coincide with the place of strongest binding (fig 2), separating between dissociated (gas–like) and associated (liquid–like) regimes in the supercritical fluid. The hydrogen bonds are thought to be symmetric from the points of view of the oxygen and hydrogen atoms. We identified an intrinsic asymmetry (fig 3) and demonstrated it is the mechanism responsible for the molecular orientation at the surface of liquid water. positive partial charge negative partial charge negative QDO beads • liquid and vapour densities (fig 2) • temperature of maximum density (fig 2) • surface tension • enthalpy of vaporisation • dielectric constant • lattice constants of ice II • radial distribution functions of supercooled, ambient and supercritical liquid locus of heat nsity e capacity maximum d s v ment o m e l dipo Figure 3: We identified molecules having five hydrogen bonds, with three neighbours bonded to the oxygen. This asymmetry is related to the mechanism for the molecular orientation at the surface of liquid water. isosurfaces of oxygen density electronic depletion electronic enhancement polarisation density T = 203 K Figure 1: Key features of electronically coarse grained water for a liquid phase cluster. T = 372 K Figure 4: Local structure of liquid water showing two motifs in the second coordination shell. Conclusion We presented a first application of Quantum Drude Oscillators to a complex molecule, namely water. The method’s success in describing such a challenging system indicates it can be applied more generally to problems in materials science, such as understanding the movement of ions in electrolytes and modelling methane, as the first application to a hydrocarbon. This method is also part of IBM’s offering of HPC solutions, with the plan for integration with the Watson cognitive platform. The aim is to combine Watson’s ability to understand complex scientific literature with accurate computer models to create a materials discovery platform. The envisaged applications are the search for antimicrobial peptides and new porous materials for batteries. References Acknowledgements Sokhan V, Jones A, Cipcigan F, Crain J, Martyna G (2015) Molecular-Scale Remnants of the Liquid-Gas Transition in Supercritical Polar Fluids. Phys Rev Lett 115. Cipcigan F, Sokhan V, Jones A, Crain J, Martyna G (2015) Hydrogen bonding and molecular orientation at the liquid–vapour interface of water. Phys Chem Chem Phys 17:8660. Sokhan V, Jones A, Cipcigan F, Crain J, Martyna G (2015) Signature properties of water:Their molecular electronic origins. Proc Natl Acad Sci 112:6341. Jones A, Cipcigan F, Sokhan V, Crain J, Martyna G (2013) Electronically Coarse-Grained Model for Water. Phys Rev Lett 110.