TY - JOUR A1 - Zhou, X. A1 - Wei, Y. A1 - Kühbach, M. A1 - Zhao, H. A1 - Vogel, F. A1 - Darvishi Kamachali, Reza A1 - Thompson, G. B. A1 - Raabe, D. A1 - Gault, B. T1 - Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data JF - Acta materialia N2 - Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations. KW - Machine learning KW - Digitalization KW - Alloy microstructure PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543049 DO - https://doi.org/10.1016/j.actamat.2022.117633 SN - 1359-6454 VL - 226 SP - 1 EP - 15 PB - Elsevier CY - Amsterdam AN - OPUS4-54304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Darvishi Kamachali, Reza A1 - da Silva, A. A1 - McEniry, E. A1 - Gault, B. A1 - Neugebauer, J. A1 - Raabe, D. T1 - Segregation-assisted spinodal and transient spinodal phase separation at grain boundaries JF - NPJ Computational Materials N2 - Segregation to grain boundaries affects their cohesion, corrosion, and embrittlement and plays a critical role in heterogeneous nucleation. In order to quantitatively study segregation and low-dimensional phase separation at grain boundaries, here, we apply a density-based phase-field model. The current model describes the grain-boundary thermodynamic properties based on available bulk thermodynamic data, while the grain-boundary-density profile is obtained using atomistic simulations. To benchmark the performance of the model, Mn grain-boundary segregation in the Fe–Mn system is studied. 3D simulation results are compared against atom probe tomography measurements conducted for three alloy compositions. We show that a continuous increase in the alloy composition results in a discontinuous jump in the segregation isotherm. The jump corresponds to a spinodal Phase separation at grain boundary. For alloy compositions above the jump, we reveal an interfacial transient spinodal phase separation. The transient spinodal phenomenon opens opportunities for knowledge-based microstructure design through the chemical manipulation of grain boundaries. The proposed density-based model provides a powerful tool to study thermodynamics and kinetics of segregation and phase changes at grain boundaries. KW - Grain Boundary Spinodal KW - Densty-based Thermodynamics KW - Microstrucrue Design PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-519497 DO - https://doi.org/10.1038/s41524-020-00456-7 VL - 6 IS - 1 SP - 191 PB - Nature AN - OPUS4-51949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zhou, X. A1 - Darvishi Kamachali, Reza A1 - Boyce, B.L. A1 - Clark, B.G. A1 - Raabe, D. A1 - Thompson, G.B. T1 - Spinodal Decomposition in Nanocrystalline Alloys JF - Acta Materialia N2 - For more than half a century, spinodal decomposition has been a key phenomenon in considering the formation of secondary phases in alloys. The most prominent aspect of the spinodal phenomenon is the lack of an energy barrier on its transformation pathway, offering an alternative to the nucleation and growth mechanism. The classical description of spinodal decomposition often neglects the influence of defects, such as grain boundaries, on the transformation because the innate ability for like-atoms to cluster is assumed to lead the process. Nevertheless, in nanocrystalline alloys, with a high population of grain boundaries with diverse characters, the structurally heterogeneous landscape can greatly influence the chemical decomposition behavior. Combining atom-probe tomography, precession electron diffraction and density-based phase-field simulations, we address how grain boundaries contribute to the temporal evolution of chemical decomposition within the miscibility gap of a Pt-Au nanocrystalline system. We found that grain boundaries can actually have their own miscibility gaps profoundly altering the spinodal decomposition in nanocrystalline alloys. A complex realm of multiple interfacial states, ranging from competitive grain boundary segregation to barrier-free low-dimensional interfacial decomposition, occurs with a dependency upon the grain boundary character. KW - Density-based Thermodynamics KW - Nanocrystalline alloys KW - Spinodal decomposition KW - Defects engineering PY - 2021 DO - https://doi.org/10.1016/j.actamat.2021.117054 VL - 215 SP - 117054 PB - Elsevier Ltd. AN - OPUS4-52918 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -