Related papers: Protocol for Clustering 4DSTEM Data for Phase Diff…
Based on the results of a previous paper (Paper I), by performing the geometrical mapping via coherent states, phase transitions are investigated and compared within two algebraic cluster models. The difference between the Semimicroscopic…
Accurate description of crystal structures is a prerequisite for predicting the physicochemical properties of materials. However, conventional X-ray diffraction (XRD) characterization often encounters intrinsic bottlenecks when applied to…
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic…
Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified.…
Structural and compositional inhomogeneity is common in zeolites and considerably affects their properties. Conventional transmission electron microscopy (TEM) cannot provide sufficient information on local structures in zeolites due to the…
We formalize within the percolation scheme, that operates along the linear chain approximation, namely at one dimension (1D), an intrinsic ability behind Raman scattering to achieve a quantitative insight into local clustering or…
The emergence of deep learning has brought the long-standing goal of comprehensively understanding and exploring crystalline materials closer to reality. Yet, universal exploration across all elements remains hindered by the combinatorial…
In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two…
Clustering is widely used for unsupervised structure discovery, yet it offers limited insight into how reliable each individual assignment is. Diagnostics, such as convergence behavior or objective values, may reflect global quality, but…
A suite of acquisition applications related to the 4D-STEM technique is presented as a software package written within the Digital Micrograph environment, which is a widely used software platform in worldwide electron microscopy…
Four-dimensional Scanning Transmission Electron Microscopy (4D-STEM) is a powerful technique for high-resolution and high-precision materials characterization at multiple length scales, including the characterization of beam-sensitive…
An analysis of the glass transition of four materials with similar chemical structures is performed: PET, PEN and two PET/PEN blends (90/10 and 70/30 w/w). During the melt processing of the blends transesterification reactions yield block…
Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently…
Boosted by the relentless increase of available computational resources, high-throughput calculations based on first principles methods have become a powerful tool to screen a huge range of materials. The backbone of these studies are…
Composition, atomic structure, and electronic properties of TM$_x$Mg$_y$O$_z$ clusters (TM = Cr, Ni, Fe, Co, $x+y \leq 3$) at realistic temperature $T$ and partial oxygen pressure $p_{\textrm{O}_2}$ conditions are explored using the {\em ab…
The composition, crystal structure and precipitation crystallography of a newly found precipitate are characterized by a Cs-corrected scanning transmission electron microscopy (STEM). The composition of the plate-like precipitate could be…
Conventionally, CMD analyses of nearby star clusters are based on observations in 2 passbands. They are plagued by considerable degeneracies between age, metallicity, distance (and extinction) that can largely be resolved by including…
We consider the problem of multiway clustering in the presence of unknown degree heterogeneity. Such data problems arise commonly in applications such as recommendation system, neuroimaging, community detection, and hypergraph partitions in…
Recent advances in scanning transmission electron microscopy (STEM) allow to observe solid-state transformations and reactions in materials induced by thermal stimulus or electron beam on the atomic level. However, despite the rate at which…
Computer simulations have long been key to understanding and designing phase-change materials (PCMs) for memory technologies. Machine learning is now increasingly being used to accelerate the modelling of PCMs, and yet it remains…