AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy.

Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

Observation of Sub-10 nm Transition Metal Dichalcogenide Nanocrystals in Rapidly Heated van der Waals Heterostructures.

Two-dimensional materials, such as transition metal dichalcogenides (TMDCs), have the potential to revolutionize the field of electronics and photonics due to their unique physical and structural properties. This research presents a novel method for synthesizing crystalline TMDCs crystals with <10 nm size using ultrafast migration of vacancies at elevated temperatures. Through in situ and ex situ processing and using atomic-level characterization techniques, we analyzed the shape, size, crystallinity, composition, and strain distribution of these nanocrystals. These nanocrystals exhibit electronic structure signatures that differ from the 2D bulk: i.e., uniform mono- and multilayers. Further, our in situ, vacuum-based synthesis technique allows observation and comparison of defect and phase evolution in these crystals formed under van der Waals heterostructure confinement versus unconfined conditions. Overall, this research demonstrates a solid-state route to synthesizing uniform nanocrystals of TMDCs and lays the foundation for materials science in confined 2D spaces under extreme conditions.

Surface Rearrangement and Sublimation Kinetics of Supported Gold Nanoparticle Catalysts.

Heterogeneous catalysts consisting of supported metallic nanoparticles typically derive exceptional catalytic activity from their large proportion of undercoordinated surface sites which promote adsorption of reactant molecules. Simultaneously, these high energy surface configurations are unstable, leading to nanoparticle growth or degradation and eventually a loss of catalytic activity. Surface morphology of catalytic nanoparticles is paramount to catalytic activity, selectivity, and degradation rates, however it is well-known that harsh reaction conditions can cause the surface structure to change. Still, limited research has focused on understanding the link between nanoparticle surface facets and degradation rates or mechanisms. Here, we study a model Au supported catalyst system over a range of temperatures using a combination of in situ transmission electron microscopy, kinetic Monte Carlo simulations, and density functional theory calculations to establish an atomistic picture of how variations in surface structures and atomic coordination environments lead to shifting evolution mechanisms as a function of temperature. By combining experimental results, which yield direct observation of dynamic shape changes and particle sublimation rates, with computational techniques, which enable understanding the fundamental thermodynamics and kinetics of nanoparticle evolution, we illustrate a two-step evolution mechanism in which mobile adatoms form through desorption from low-coordination facets and subsequently sublimate off the particle surface. By understanding the role of temperature in the competition between surface diffusion and sublimation, we are able to show how individual atomic movements lead to particle scale morphological changes and rationalize why sublimation rates vary between particles in a system of nearly identical nanoparticles.

Quantifying Competitive Degradation Processes in Supported Nanocatalyst Systems.

The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing in situ transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Data mining of particle evolution statistics allows us to determine physically reasonable values for the model parameters, quantify the particle size at which the Gibbs-Thomson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. This approach can be applied to a wide range of supported nanoparticle systems, allowing quantitative insight into the mechanisms that control their evolution in reactive environments.