Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions

Fe-Ni bazlı nanoalaşımların, modellenmesi simülasyonu, sentezlenmesi ve yapısal özelliklerinin belirlenmesi.

There is a growing interest in the simulation and production of nanoalloys because the unique chemical and physical properties of nanoalloys can be tuned, and completely new structural motifs can be created by varying the type and composition of constituent elements, the atomic ordering, size, and shape of the nanoparticles. As an important magnetic material, Fe-Ni based nanoalloys have promising applications in the chemical industry, aerospace and stealth industry, magnetic biomedical applications and computer hardware industry. The purpose of this study is to analyze the structural properties of the magnetic nanoalloys at atomistic level and to establish a bridge between theoretical and experimental studies, in order to interpret many of experimental results and to predict the physical and chemical properties of the nanoalloys. In the theoretical part, structural evolutions of Fe-Ni based nanoalloys have been studied by using molecular dynamics (MD) method in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). In this regard, structural evolution of the bimetallic FeNi3 crystalline and amorphous nanoalloys has been investigated by means of MD simulation combined with Embedded Atom Model (EAM) with taking into account the effect of temperature (300-1700 K), particle size (2 nm-6 nm) and shape (spherical and cubic) on radial distribution functions, inter-atomic distances, coordination numbers, core-to-surface concentration profiles, surface energies and Voronoi analysis. From the molecular dynamics simulations, it has been clearly observed that the structural evolution, melting point and atomic arrangements of the nanoparticles exhibited strongly size and shape dependent behavior. As the particle size of the simulated nanoparticles increased, the particles became more heat-resistant and mostly preserved their stable crystalline structure, shape and mixing pattern at high temperatures. Also, it has been observed that the 6 nm nanoparticles owned the FCC lattice structure at room temperature which is consistent with the L12-type ordered structure of the synthesized via mechanical alloying FeNi3 nanoparticles with soft magnetic properties. In the experimental part of the study, FeNi3 bimetallic nanoalloys were synthesized via mechanical alloying in a planetary high energy ball milling. The experimental studies were carried out in three parts. Firstly, mechanical alloying in high energy dry planetary ball milling with 250 and 400 rpm was applied to obtain FeNi3 nanoparticles. Afterward, two-step mechanical alloying was performed in which dry milling was followed by surfactant-assisted ball milling to investigate the surfactant (oleic acid and oleylamine) and solvent (heptane) effect on the structure, size, and properties of the FeNi3 nanoalloys. The structural and magnetic properties of the alloyed nanoparticles have been analyzed using XRD, SEM, EDS, and VSM techniques. In terms of the particle size, it was found that the amount of nano-sized particles raised with increasing milling time and milling speed, and consequently the magnetic properties of the particles varied. However, no significant effect of surfactants on the particle size was observed. The smallest, L12-type ordered FeNi3 nanopowders with 5.82 nm crystallite size, -0.46% strain value, and 3.54263 Å lattice parameter, showing soft magnetic properties, were synthesized by mechanical alloying with 400rpm under dry atmosphere after 80 h milling time.M.S. - Master of Scienc

Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm

We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses

Sampling real-time atomic dynamics in metal nanoparticles by combining experiments, simulations, and machine learning

The atomic dynamics of metal nanoparticles (NPs), prominent already at low temperatures, is crucial for their properties but also challenging to elucidate. Recent advances in experimental approaches may provide atomically resolved snapshots of the structure of NPs in relevant regimes, but limitations in experimental data acquisition hinder the reconstruction of the atomic dynamics present within them. Molecular simulations -- typically starting from ideal/perfect NP structures -- allow tracking the motion of atoms over time, but suffer from limited sampling and provide results that, being dependent on the initial (putative) structure, are often only indicative. Here, combining state-of-the-art experimental and computational approaches, we demonstrate how it is possible to tackle the inherent limitations of both methods and resolve the atomistic dynamics present in metal NPs under realistic conditions. Annular dark-field scanning transmission electron microscopy (ADF-STEM) enables the acquisition of a time series of ten high-resolution images of an Au NP. Each image is taken at intervals of 0.6 seconds, providing data on a second timescale during the experimental sampling. These are used to reconstruct atomistic 3D structures of the real NP that are then used as starting configurations for ten independent molecular dynamics (MD) simulations. Unsupervised machine learning analysis of the data extracted from the MD trajectories using advanced structural and dynamical descriptors allows tracking and resolving the real-time atomic dynamics present within the NP under relevant conditions. This provides new perspectives into the realistic atomic dynamics within such NPs. We expect that such integrated experimental/computational approaches will become fundamental in various fields where the dynamics of NPs plays a key role, from catalysis to, e.g., nanoelectronics and biomedicine

Sampling real-time atomic dynamics in metal nanoparticles by combining experiments, simulations, and machine learning

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. imag

Data for Sampling Real\u2010Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic\u2010resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state\u2010of\u2010the\u2010art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark\u2010field scanning transmission electron microscopy enables the acquisition of ten high\u2010resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real\u2010time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions