Despite its relevance in biology and engineering, the molecular mechanism driving cavitation in water remains unknown. Using computer simulations, we investigate the structure and dynamics of vapor bubbles emerging from metastable water at negative pressures. We find that in the early stages of cavitation, bubbles are irregularly shaped and become more spherical as they grow. Nevertheless, the free energy of bubble formation can be perfectly reproduced in the framework of classical nucleation theory (CNT) if the curvature dependence of the surface tension is taken into account. Comparison of the observed bubble dynamics to the predictions of the macroscopic Rayleigh-Plesset (RP) equation, augmented with thermal fluctuations, demonstrates that the growth of nanoscale bubbles is governed by viscous forces. Combining the dynamical prefactor determined from the RP equation with CNT based on the Kramers formalism yields an analytical expression for the cavitation rate that reproduces the simulation results very well over a wide range of pressures. Furthermore, our theoretical predictions are in excellent agreement with cavitation rates obtained from inclusion experiments. This suggests that homogeneous nucleation is observed in inclusions, whereas only heterogeneous nucleation on impurities or defects occurs in other experiments.cavitation | water | negative pressure | bubble nucleation | liquid-vapor transition D ue to its pronounced cohesion, water remains stable under tension for long times. Experimentally, strongly negative pressures exceeding −120 MPa (1-6) can be sustained before the system decays into the vapor phase via cavitation, i.e., bubble nucleation. Recently, cavitation in water under tension has drawn research interest due to its importance in biological processes, like water transport in natural (7-10) and synthetic (11, 12) trees, spore propagation of ferns (13), and poration of cell membranes (14, 15). Furthermore, cavitation in water appears to be the driving force behind the sonocrystallization of ice (16,17), and preventing its occurrence remains a challenge in turbine and propeller design (18). Studying the onset of cavitation has also proven to be a valuable tool to locate the line of density maxima in metastable water (4), which contributes to the ongoing effort of explaining the origin of water's anomalies (6,19). Interest in the topic is magnified by the startling discrepancy arising when cavitation in water is investigated using different experimental methods. Although agreement between different methods is excellent in the high-temperature regime, where the liquid is unable to sustain large tension, a significantly higher degree of metastability is reached when studying cavitation in inclusions along an isochoric path (1-5) compared with other techniques (20, 21) at low temperatures (22).Due to the short time scale on which the transition takes place and the small volume of the critical bubble at experimentally feasible conditions, direct observation of cavitation at the microscopic level...

The accurate identification and classification of local ordered and disordered structures is an important task in atomistic computer simulations. Here, we demonstrate that properly trained artificial neural networks can be used for this purpose. Based on a neural network approach recently developed for the calculation of energies and forces, the proposed method recognizes local atomic arrangements from a set of symmetry functions that characterize the environment around a given atom. The algorithm is simple and flexible and it does not rely on the definition of a reference frame. Using the Lennard-Jones system as well as liquid water and ice as illustrative examples, we show that the neural networks developed here detect amorphous and crystalline structures with high accuracy even in the case of complex atomic arrangements, for which conventional structure detection approaches are unreliable.

Free-energy differences computed from fast-switching simulations or measurements according to the Jarzynski equation are independent of the particular protocol specifying how the control parameter is changed in time. In contrast, the average work carried out on the system as well the accuracy of the resulting free energy strongly depend on the protocol. Recently, Schmiedl and Seifert [Phys. Rev. Lett. 98, 108301 (2007)] found that protocols that minimize the average work for a given duration of the switching process have discrete steps at the beginning and the end. Here we determine numerically the protocols that minimize the statistical error in the free energy estimate and find that such minimum error protocols have similar discrete jumps. Our analysis shows that the reduction in computational effort achieved by the use of steplike protocols can be considerable. Such large savings of computing time, however, typically occur for parameter ranges in which an application of the Jarzynski equation is impractical due to large statistical errors arising from the exponential work average.

Several proton-disordered crystalline ice structures are known to proton order at sufficiently low temperatures, provided that the right preparation procedure is used. For cubic ice, ice Ic, however, no proton ordering has been observed so far. Here, we subject ice Ic to an experimental protocol similar to that used to proton order hexagonal ice. In situ FT-IR spectroscopy carried out during this procedure reveals that the librational band of the spectrum narrows and acquires a structure that is observed neither in proton-disordered ice Ic nor in ice XI, the proton-ordered variant of hexagonal ice. On the basis of vibrational spectra computed for ice Ic and four of its proton-ordered variants using classical molecular dynamics and ab initio simulations, we conclude that the features of our experimental spectra are due to partial proton ordering, providing the first evidence of proton ordering in cubic ice. We further find that the proton-ordered structure with the lowest energy is ferroelectric, while the structure with the second lowest energy is weakly ferroelectric. Both structures fit the experimental spectral similarly well such that no unique assignment of proton order is possible based on our results.

Depending on initial conditions, individual finite time trajectories of dynamical systems can have very different chaotic properties. Here we present a numerical method to identify trajectories with atypical chaoticity, pathways that are either more regular or more chaotic than average. The method is based on the definition of an ensemble of trajectories weighted according to their chaoticity, the Lyapunov weighted path ensemble. This ensemble of trajectories is sampled using algorithms borrowed from transition path sampling, a method originally developed to study rare transitions between long-lived states. We demonstrate our approach by applying it to several systems with numbers of degrees of freedom ranging from one to several hundred and in all cases the algorithm found rare pathways with atypical chaoticity. For a double-well dimer embedded in a solvent, which can be viewed as simple model for an isomerizing molecule, rare reactive pathways were found for parameters strongly favoring chaotic dynamics.