High catalytic efficiency in metal nanocatalysts is attributed to large surface area to volume ratios and an abundance of under-coordinated atoms that can decrease kinetic barriers. Although overall shape or size changes of nanocatalysts have been observed as a result of catalytic processes, structural changes at low-coordination sites such as edges, remain poorly understood. Here, we report high-lattice distortion at edges of Pt nanocrystals during heterogeneous catalytic methane oxidation based on in situ 3D Bragg coherent X-ray diffraction imaging. We directly observe contraction at edges owing to adsorption of oxygen. This strain increases during methane oxidation and it returns to the original state after completing the reaction process. The results are in good agreement with finite element models that incorporate forces, as determined by reactive molecular dynamics simulations. Reaction mechanisms obtained from in situ strain imaging thus provide important insights for improving catalysts and designing future nanostructured catalytic materials.

Minimizing friction and wear at a rubbing interface continues to be a challenge and has resulted in the recent surge toward the use of coatings such as diamond-like carbon (DLC) on machine components. The problem with the coating approach is the limitation of coating wear life. Here, we report a lubrication approach in which lubricious, wear-protective carbon-containing tribofilms can be self-generated and replenishable, without any surface pretreatment. Such carbon-containing films were formed under modest sliding conditions in a lubricant consisting of cyclopropanecarboxylic acid as an additive dissolved in polyalphaolefin base oil. These tribofilms show the same Raman D and G signatures that have been interpreted to be due to the presence of graphite- or DLC films. Our experimental measurements and reactive molecular dynamics simulations demonstrate that these tribofilms are in fact high-molecular weight hydrocarbons acting as a solid lubricant.

We use molecular dynamics simulations to investigate the cavitation dynamics around intensely heated solid nanoparticles immersed in a model Lennard-Jones fluid. Specifically, we study the temporal evolution of vapor nanobubbles that form around the solid nanoparticles heated over ps time scale and provide a detail description of the following vapor formation and collapse. For 8 nm diameter nanoparticles we observe the formation of vapor bubbles when the liquid temperature 0.5-1 nm away from the nanoparticle surface reaches ∼90% of the critical temperature, which is consistent with the onset of spinodal decomposition. The peak heat flux from the hot solid to the surrounding liquid at the bubble formation threshold is ∼20 times higher than the corresponding steady state critical heat flux. Detailed analysis of the bubble dynamics indicates adiabatic formation followed by an isothermal final stage of growth and isothermal collapse.

We introduce a bond order potential (BOP) for stanene based on an ab initio derived training data set. The potential is optimized to accurately describe the energetics, as well as thermal and mechanical properties of a free-standing sheet, and used to study diverse nanostructures of stanene, including tubes and ribbons. As a representative case study, using the potential, we perform molecular dynamics simulations to study stanene's structure and temperature-dependent thermal conductivity. We find that the structure of stanene is highly rippled, far in excess of other 2-D materials (e.g., graphene), owing to its low in-plane stiffness (stanene: ∼ 25 N/m; graphene: ∼ 480 N/m). The extent of stanene's rippling also shows stronger temperature dependence compared to that in graphene. Furthermore, we find that stanene based nanostructures have significantly lower thermal conductivity compared to graphene based structures owing to their softness (i.e., low phonon group velocities) and high anharmonic response. Our newly developed BOP will facilitate the exploration of stanene based low dimensional heterostructures for thermoelectric and thermal management applications.

The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle selfassembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in realtime 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our datadriven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to twodimensional (2D) materials.