The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy
data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and un-supervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co3Sn2S2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co3Sn2S2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.
Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on
a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow is universal and can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.
The electronic structure inhomogeneities in Co, Ni, and Cr doped BaFe2As2 122 single crystals are compared using scanning tunneling microscopy/spectroscopy (STM/S) at the nanoscale within three bulk property regions in the phase diagram: a pure super
conducting (SC) dome region (Co-122), a coexisting SC and antiferromagnetic (AFM) region (Ni-122), and a non-SC region (Cr-122). Machine learning is utilized to categorize the various nanometer scale inhomogeneous electronic states, described here as in-gap, L-shape and S-shape states immersed into the SC matrix for Ni-and Co-doped 122, and L-shape and S-shape states into the metallic matrix for Cr-doped 122. Although the relative percentages of in-gap, L-shape and S-shape states vary in the three samples, the total volume fraction of the three electronic states is quite similar. This is coincident with the number of electrons (Ni0.04 and Co0.08) and holes (Cr0.04) doped into the 122 compound. By combining the volume fractions of the three states, the local density of states (LDOS), magnetic field dependent behavior and global properties in these three samples, the in-gap state is confirmed as a magnetic impurity state from the Co or Ni dopants. In addition, the L-shape state is identified as a spin density wave (SDW) which competes with the SC phase, and the S-shape state is found to be another form of magnetic order which constructively cooperates with the SC phase, rather than competing with it. The comparison of the vortex structures indicates that the inhomogeneous electronic states serve as pinning centers for stabilizing the vortex lattice.
The aim of tool path planning is to maximize the efficiency against some given precision criteria. In practice, scallop height should be kept constant to avoid unnecessary cutting, while the tool path should be smooth enough to maintain a high feed r
ate. However, iso-scallop and smoothness often conflict with each other. Existing methods smooth iso-scallop paths one-by-one, which make the final tool path far from being globally optimal. This paper proposes a new framework for tool path optimization. It views a family of iso-level curves of a scalar function defined over the surface as tool path so that desired tool path can be generated by finding the function that minimizes certain energy functional and different objectives can be considered simultaneously. We use the framework to plan globally optimal tool path with respect to iso-scallop and smoothness. The energy functionals for planning iso-scallop, smoothness, and optimal tool path are respectively derived, and the path topology is studied too. Experimental results are given to show the effectiveness of the proposed methods.
Combined scanning tunneling microscopy, spectroscopy and local barrier height (LBH) studies show that low-temperature-cleaved optimally-doped Ba(Fe1-xCox)2As2 crystals with x=0.06, with Tc = 22 K, have complicated morphologies. Although the cleavage
surface and hence the morphologies are variable, the superconducting gap maps show the same gap widths and nanometer size inhomogeneities irrelevant to the morphology. Based on the spectroscopy and LBH maps, the bright patches and dark stripes in the morphologies are identified as Ba and As dominated surface terminations, respectively. Magnetic impurities, possibly due to cobalt or Fe atoms, are believed to create local in-gap state and in addition suppress the superconducting coherence peaks. This study will clarify the confusion on the cleavage surface terminations of the Fe-based superconductors, and its relation with the electronic structures.
