Single-particle imaging using X-ray free-electron lasers is an emerging technique that could provide high-resolution structures of macromolecules in the gas phase. One of the largest difficulties in realizing this goal is the unknown orientation of the individual sample molecules at the time of exposure. Preorientation of the molecules has been identified as a possible solution to this problem. Using molecular dynamics simulations, we identify a range of electric field strengths where proteins become oriented without losing their structure. For a number of experimentally relevant cases we show that structure determination is possible only when orientation information is included in the orientation-recovery process. We conclude that nondestructive field orientation of intact proteins is feasible and that it enables a range of new structural investigations with single-particle imaging.

In both its gaseous and condensed forms, carbon dioxide has an everincreasing impact on Earth's chemistry and human life and activities. However, many aspects of its high-pressure phase diagram remain unclear. In this work, we present a complete structural characterization of carbon dioxide fluids under geological conditions using extensive ab initio molecular dynamics simulations throughout a wide pressure and temperature range, corresponding to Earth's lower mantle. We identify and describe four different disordered regimes, including two polymeric forms and two molecular ones, all within the geothermal conditions of the lower mantle. At pressures below 40 GPa, we find that the molecular liquid becomes very reactive above 2000 K: the C−O double bond routinely breaks, resulting in small and transient chains composed of CO 2 units and frequently leading to an exchange of oxygen atoms between molecules. At higher pressures, in addition to the polymeric fluid previously reported at 3000 K, we find a polymeric system with glass-like behavior at lower temperatures, suggesting a complex interplay between kinetics and stability.

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We propose an efficient method to explore the configuration space of nanoclusters by combining together ab initio molecular dynamics, metadynamics and data clustering algorithms. On one side, we employ collective variables sensitive to topological changes in the network of interatomic connections to map the configuration space; on the other, we introduce an automatic approach to select, in such space, representative structures to be optimized. In this way, we show that it is possible to sample thoroughly the set of relevant nanocluster geometries, at a limited computational cost. We apply our method to explore MoS 2 clusters, that recently raised a sizable interest due to their remarkable electronic and catalytic properties. We demonstrate that the unsupervised algorithm is able to find a large number of low-energy structures at different cluster sizes, including both bulk-like geometries and very different topologies. We are thus able to recapitulate, in a single computational study on technologically-relevant MoS 2 clusters, the results of all previous works that employed distinct techniques like genetic algorithms or heuristic hypotheses. Furthermore, we found several new structures not previously reported. The ensemble of MoS 2 cluster structures is deposited in a publicly accessible database.

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