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The investigation of the microscopic processes underlying structural phase transformations in solids is extremely challenging for both simulation and experiment. Atomistic simulations of solid-solid phase transitions require extensive sampling of the corresponding high-dimensional and often rugged energy landscape. Here, we propose a rigorous construction of a 1D path collective variable that is used in combination with enhanced sampling techniques for efficient exploration of the transformation mechanisms. The path collective variable is defined in a space spanned by global classifiers that are derived from local structural units. A reliable identification of the local structural environments is achieved by employing a neural-network-based classification scheme. The proposed path collective variable is generally applicable and enables the investigation of both transformation mechanisms and kinetics.

Water exhibits numerous anomalous properties, many of which remain poorly understood. One of its intriguing behaviors is that it exhibits a temperature of maximum density (TMD) at 4 °C. We provide here new experimental evidence for hitherto unknown abrupt changes in proton transfer kinetics at the TMD. In particular, we show that the lifetime of OH^{-} ions has a maximum at this temperature, in contrast to hydronium ions. Furthermore, base-catalyzed proton transfer shows a sharp local minimum at this temperature, and activation energies change abruptly as well. The measured lifetimes agree with earlier theoretical predictions as the temperature approaches the TMD. Similar results are also found for heavy water at its own TMD. These findings point to a high propensity of forming fourfold coordinated OH^{-} solvation complexes at the TMD, underlining the asymmetry between hydroxide and hydronium transport. These results could help to further elucidate the unusual properties of water and related liquids.

The generation of free energy landscapes corresponding to conformational equilibria in complex molecular systems remains a significant computational challenge. Adding to this challenge is the need to represent, store, and manipulate the often high-dimensional surfaces that result from rare-event sampling approaches employed to compute them. In this Letter, we propose the use of artificial neural networks as a solution to these issues. Using specific examples, we discuss network training using enhanced-sampling methods and the use of the networks in the calculation of ensemble averages.

The ability of certain organic molecules to form multiple crystal structures, known as polymorphism, has important ramifications for pharmaceuticals and high energy materials. Here, we introduce an efficient molecular dynamics method for rapidly identifying and thermodynamically ranking polymorphs. The new method employs high temperature and adiabatic decoupling to the simulation cell parameters in order to sample the Gibbs free energy of the polymorphs. Polymorphism in solid benzene is revisited, and a resolution to a long-standing controversy concerning the benzene II structure is proposed.

First-principles molecular dynamics calculations performed in a fully converged basis set are used to reveal new details about the mechanism of the anomalous proton-transport process in water, a fundamental question dating back over 200 years. By separating actual structural diffusion from simple rattling events, wherein a proton shuttles forth and back in a hydrogen bond, it is found that the former are driven by a concerted mechanism in which hydronium begins to accept a hydrogen bond from a donor water molecule while the proton-receiving water molecule simultaneously loses one of its acceptor hydrogen bonds. The kinetics of the process are found to be in good agreement with recent experiments.

A theory based on population correlation functions is introduced for connecting solvation topologies and microscopic mechanisms to transport kinetics of charge defects in hydrogen-bonded networks. The theory is tested on the hydrated proton by extracting a comprehensive set of relaxation times, lifetimes, and rates from ab initio molecular dynamics simulations and comparing to recent femtosecond experiments. When applied to the controversial case of the hydrated hydroxide ion, the theory predicts that only one out of three proposed transport models is consistent with known experimental data.

One of the computational "grand challenges" is to develop methodology capable of sampling conformational equilibria in systems with rough energy landscapes. Here, a significant step forward is made by combining molecular dynamics with a novel variable transformation designed to enhance sampling by reducing barriers without introducing bias and, thus, to preserve, perfectly, equilibrium properties.