Phases of the Au(100) surface reconstruction

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.

Casimir-Induced Instabilities at Metallic Surfaces and Interfaces

Surface distortion splits surface plasmons asymmetrically in energy with a net lowering of zero-point energy. We contrast this with the symmetrical distortion of electronic energy levels. We use conformal mapping to demonstrate this splitting and find that surface corrugation always leads to a decrease in the zero-point energy of a metallic surface, but the decrease is not strong enough to drive a surface reconstruction on its own. A second metallic surface in proximity to the first gives a more significant lowering of energy, sufficient to drive the instability of a mercury thin film. This mechanism provides a fundamental length scale limit to planar nanostructures.

Line Tension and Wettability of Nanodrops on Curved Surfaces

In this work we study the formation of nanodrops on curved surfaces (both convex and concave) by means of molecular dynamics simulations, where the particles interact via a Lennard-Jones potential. We find that the contact angle is not affected by the curvature of the substrate, in agreement with previous experimental findings. This means that the change in curvature of the drop in response to the change in curvature of the substrate can be predicted from simple geometrical considerations, under the assumption that the drop's shape is a spherical cap, and that the volume remains unchanged through the curvature. The resulting prediction is in perfect agreement with the simulation results, for both convex and concave substrates. In addition, we calculate the line tension, namely, by fitting the contact angle for different size drops to the modified Young equation. We find that the line tension for concave surfaces is larger than for convex surfaces, while for zero curvature it has a clear maximum. This feature is found to be correlated with the number of particles in the first layer of the liquid on the surface.

Simultaneous investigation of the structure and surface of a Co/alumina catalyst during Fischer–Tropsch synthesis: discrimination of various phenomena with beneficial or disadvantageous impact on activity

Simultaneous investigation of structure and surface of a Co/Al₂O₃ catalyst during Fischer–Tropsch synthesis: identification of beneficial and disadvantageous phenomena.

Overlayer strain relief on surfaces with square symmetry: phase diagram for a 2D Frenkel-Kontorova model

Overlayers on surfaces with square symmetry exhibit a huge variety of strain relief mechanisms. I present a simple 2D Frenkel-Kontorova model and calculate the associated zero temperature phase diagram which shows a transition from overlayers with square symmetry (and possible square dislocation patterns) to hexagonal symmetry. The phase diagram includes the experimentally observed clock-rotated phase. Local density approximation calculations suggested by the model show that a clean Ni(100) surface reconstructs from a bulk-terminated to a clock-rotated structure at biaxial compressive strains above 2.5%.