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Ali Nematollahi

Publications and source records attributed to Ali Nematollahi.

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Dissecting functional degradation in NiTi shape-memory-alloys containing amorphous regions via atomistic simulations

Molecular dynamics simulations are performed to provide a detailed understanding of the functional degradation of shape memory alloys at small scale. The origin of the experimentally reported accumulation of plastic deformation and the anomalous sudden increase of the residual strain under cyclic mechanical loading are explained by detailed insights into the relevant atomic scale processes. Our work reveals that the mechanical response of shape-memory-alloy pillars under cyclic compression is significantly influenced by the presence of an amorphous-like surface region as experimentally induced by focused ion beam milling. The main factor responsible for the observed degradation of superelasticity under cyclic loading is the accumulated plastic deformation and the resultant retained martensite originating from a synergetic contribution of the amorphous and crystalline shape-memory-alloy regions. We show that the reported sudden diminishment of the stress plateaus and hysteresis under cyclic loading is caused by the increased stability of the martensite phase due to the presence of the amorphous phase. Based on the identified mechanism responsible for the degradation, we validate reported methods of recovering the superelasticity and propose a new method to prohibit the synergetic contribution of the amorphous and crystalline regions, such as to achieve a sustainable operation of shape memory alloys at small scale.

cond-mat.mtrl-sci

Advanced data mining in field ion microscopy

Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM.

physics.app-ph