TY - JOUR A1 - Suárez Ocano, Patricia A1 - Agudo Jácome, Leonardo A1 - Lopez-Galilea, I. A1 - Darvishi Kamachali, Reza A1 - Fries, S. G. T1 - Data regarding the experimental findings compared with CALPHAD calculations of the AlMo0.5NbTa0.5TiZr refractory high entropy superalloy N2 - This contribution contains the raw data used to compare experimental results with thermodynamic calculations using the CALPHAD method, which is related to the research article “The AlMo0.5NbTa0.5TiZr refractory high entropy superalloy: experimental findings and comparison with calculations using the CALPHAD method” [1] , and therefore this article can be used as a basis for interpreting the data contained therein. The AlMo0.5NbTa0.5TiZr refractory superalloy was characterized in the cast and annealed condition (1400 °C for 24 h) in order to measure grain size and to identify and measure the size and area fraction of the phases present. The raw data of this article include X-ray diffraction (XRD) measurements, microstructural characterization by scanning and transmission electron microscopy (SEM and TEM), and elemental analysis by energy dispersive X-ray spectroscopy (EDX). XRD includes the determination of phases and the lattice parameters (A2, B2, and hexagonal structure). Microstructural analysis by scanning and transmission electron microscopy includes (1) identification of composition, size, and volume fraction of the present phases and (2) determination of grain size. Based on these experimental data, it is possible to identify similarities and discrepancies with the data calculated using the CALPHAD method for the alloy under study in Ref. [1] , which provides the basis for better and more efficient development of reliable databases. KW - Transmission electron microscopy KW - Scanning electron microscopy KW - Microstructural characterization KW - Refractory high entropy alloys PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-568009 DO - https://doi.org/10.1016/j.dib.2022.108858 SN - 2352-3409 VL - 46 SP - 1 EP - 19 PB - Elsevier CY - Amsterdam AN - OPUS4-56800 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kianinejad, Kaveh A1 - Darvishi Kamachali, Reza A1 - Khedkar, Abhinav A1 - Manzoni, Anna Maria A1 - Agudo Jácome, Leonardo A1 - Schriever, Sina A1 - Saliwan Neumann, Romeo A1 - Megahed, Sandra A1 - Heinze, Christoph A1 - Kamrani, Sepideh A1 - Fedelich, Bernard T1 - Creep anisotropy of additively manufactured Inconel-738LC: Combined experiments and microstructure-based modeling N2 - The current lack of quantitative knowledge on processing-microstructure–property relationships is one of the major bottlenecks in today’s rapidly expanding field of additive manufacturing. This is centrally rooted in the nature of the processing, leading to complex microstructural features. Experimentally-guided modeling can offer reliable solutions for the safe application of additively manufactured materials. In this work, we combine a set of systematic experiments and modeling to address creep anisotropy and its correlation with microstructural characteristics in laser-based powder bed fusion (PBF-LB/M) additively manufactured Inconel-738LC (IN738LC). Three sample orientations (with the tensile axis parallel, perpendicular, and 45° tilted, relative to the building direction) are crept at 850 °C, accompanied by electron backscatter secondary diffraction (EBSD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) investigations. A crystal plasticity (CP) model for Ni-base superalloys, capable of modeling different types of slip systems, is developed and combined with various polycrystalline representative volume elements (RVEs) built on the experimental measurements. Besides our experiments, we verify our modeling framework on electron beam powder bed fusion (PBF-EB/M) additively manufactured Inconel-738LC. The results of our simulations show that while the crystallographic texture alone cannot explain the observed creep anisotropy, the superlattice extrinsic stacking faults (SESF) and related microtwinning slip systems play major roles as active deformation mechanisms. We confirm this using TEM investigations, revealing evidence of SESFs in crept specimens. We also show that the elongated grain morphology can result in higher creep rates, especially in the specimens with a tilted tensile axis. KW - Additive manufactured Ni-base superalloys KW - Creep KW - Crystal plasticity KW - Superlattice extrinsic stacking faults PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-601576 DO - https://doi.org/10.1016/j.msea.2024.146690 SN - 0921-5093 VL - 907 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-60157 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Suárez Ocano, Patricia A1 - Fries, S. G. A1 - Lopez-Galilea, I. A1 - Darvishi Kamachali, Reza A1 - Roik, J. A1 - Agudo Jácome, Leonardo T1 - The AlMo0.5NbTa0.5TiZr refractory high entropy superalloy: Experimental findings and comparison with calculations using the CALPHAD method N2 - Detailed microstructural characterization of the AlMo0.5NbTa0.5TiZr refractory high entropy superalloy in the as-cast state is reported for first time and compared with the state annealed at 1400 oC for 24 h. The former shows a dendritic structure, with a mixture of A2/B2 phases < 20 nm in both the dendritic and interdendritic regions. A mostly amorphous phase, rich in Al and Zr, is found within the interdendritic region. The annealed state reproduced the combination of A2/B2/Al-Zr-rich phases reported previously. Calculations from two relevant ThermoCalc databases were compared with the experimental results. Equilibrium calculations were compared with results for the annealed alloy, whereas solidification paths calculated using Scheil-Gulliver model were used for comparison with the as-cast alloy. A previously hypothesized spinodal decomposition during cooling as the mechanism responsible for the patterned A2/B2 microstructure is confirmed via the CALPHAD calculations, pointing to its use as an efficient design tool for such alloys. Finally, the comparison between the experimental and computational findings allowed better understanding the solidification path and equilibrium stability of this alloy, giving a base to make better decisions on the field of new refractory superalloy design. KW - CALPHAD database analysis KW - Refractory superalloys KW - Chemically complex alloy KW - Characterization KW - Microstructure PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-545906 DO - https://doi.org/10.1016/j.matdes.2022.110593 SN - 1873-4197 VL - 217 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam AN - OPUS4-54590 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Darvishi Kamachali, Reza T1 - Melting upon Coalescence of Solid Nanoparticles N2 - The large surface-to-volume ratio of nanoparticles is understood to be the source of many interesting phenomena. The melting temperature of nanoparticles is shown to dramatically reduce compared to bulk material. Yet, at temperatures below this reduced melting point, a liquid-like atomic arrangement on the surface of nanoparticles is still anticipated to influence its properties. To understand such surface effects, here, we study the coalescence of Au nanoparticles of various sizes using molecular dynamics simulations. Analysis of the potential energy and Lindemann index distribution across the nanoparticles reveals that high-energy, high-mobility surface atoms can enable the coalescence of nanoparticles at temperatures much lower than their corresponding melting point. The smaller the nanoparticles, the larger the difference between their melting and coalescence temperatures. For small enough particles and/or elevated enough temperatures, we found that the coalescence leads to a melting transition of the two nominally solid nanoparticles, here discussed in relation to the heat released due to the surface reduction upon the coalescence and the size dependence of latent heat. Such discontinuous melting transitions can lead to abrupt changes in the properties of nanoparticles, important for their applications at intermediate temperatures. KW - Nanoparticles KW - Molecular Dynamics KW - Surface-induced Melting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-552183 DO - https://doi.org/10.3390/solids3020025 VL - 3 IS - 2 SP - 361 EP - 373 PB - MDPI AN - OPUS4-55218 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Elfetni, Seif A1 - Darvishi Kamachali, Reza T1 - PINNs-MPF: A Physics-Informed Neural Network framework for Multi-Phase-Field simulation of interface dynamics N2 - We present PINNs-MPF framework, an application of Physics-Informed Neural Networks (PINNs) to handle Multi-Phase-Field (MPF) simulations of microstructure evolution. A combination of optimization techniques within PINNs and in direct relation to MPF method are extended and adapted. The numerical resolution is realized through a multi-variable time-series problem by using fully discrete resolution. Within each interval, space, time, and phases/grains are treated separately, constituting discrete subdomains. PINNs-MPF is equipped with an extended multi-networking (parallelization) concept to subdivide the simulation domain into multiple batches, with each batch associated with an independent NN trained to predict the solution. To ensure continuity across the spatio-temporal-phasic subdomains, a Master NN efficiently is to handle interactions among the multiple networks and facilitates the transfer of learning. A pyramidal training approach is proposed to the PINN community as a dual-impact method: to facilitate the initialization of training when dealing with multiple networks, and to unify the solution through an extended transfer of learning. Furthermore, a comprehensive approach is adopted to specifically focus the attention on the interfacial regions through a dynamic meshing process, significantly simplifying the tuning of hyper-parameters, serving as a key concept for addressing MPF problems using machine learning. We perform a set of systematic simulations that benchmark foundational aspects of MPF simulations, i.e., the curvature-driven dynamics of a diffuse interface, in the presence and absence of an external driving force, and the evolution and equilibrium of a triple junction. The proposed PINNs-MPF framework successfully reproduces benchmark tests with high fidelity and Mean Squared Error (MSE) loss values ranging from 10^−6 to 10^−4 compared to ground truth solutions. KW - Machine learning KW - PINNs KW - Phase-field method KW - Microstructure evolution KW - Parallel training KW - Neural networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629740 DO - https://doi.org/10.1016/j.enganabound.2025.106200 SN - 0955-7997 VL - 176 SP - 1 EP - 22 PB - Elsevier CY - Amsterdam AN - OPUS4-62974 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Elfetni, Seifallah A1 - Darvishi Kamachali, Reza T1 - PINNs-MPF: An Efficient Physics-Informed Machine Learning-based Solver for Multi-Phase-Field Simulations using Tensorflow N2 - This paper introduces PINNs-MPF, a novel Machine Learning-based solver designed for Multi-Phase-Field (MPF) and diffuse interface simulations, offering innovative approaches to address complex challenges in addressing microstructure evolution in polycrystalline materials using Machine Learning. The framework not only surpasses current limitations in handling multi-phase problems but also allows for potential upscaling to tackle more intricate scenarios. Developed in Python, the related code leverages optimized libraries like TensorFlow, showcasing efficiency and potential scalability in materials science and engineering simulations. This framework, integrating advanced techniques such as multi-networking and training optimization, setting a new standard in predictive capabilities and understanding complex physical phenomena. KW - Machine Learning KW - Microstructure Simulation KW - Phase Field PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631917 DO - https://doi.org/10.1016/j.simpa.2025.100753 SN - 2665-9638 VL - 24 SP - 1 EP - 4 PB - Elsevier B.V. AN - OPUS4-63191 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 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 - CONF A1 - Darvishi Kamachali, Reza T1 - On the origin of embrittlement in Mn containing and Zn-coated steels N2 - Grain boundary embrittlement in medium-Mn steels and liquid metal embrittlement (LME) in Zn-coated high strength steels are among key challenges on the way of safe application of sustainable steels for automotive industry. Using a novel density-based model for grain boundaries, we reveal that the affinity of a grain boundary to attract Mn and Zn atoms result in a segregation transition accompanied by interfacial structural changes. In case of the Zn, the simulations show that the amount of segregation abruptly increases with decreasing temperature, while the Zn content in the alloy, required for triggering the segregation transition, decreases. The results are discussed in the context of CALPHAD-integrated density-based grain boundary phase diagrams. T2 - DPG 2024 CY - Berlin, Germany DA - 17.03.2024 KW - Phase-Field Simulation KW - CALPHAD KW - Steels KW - Density-based Thermodynamics PY - 2024 AN - OPUS4-60743 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Li, L. A1 - Darvishi Kamachali, Reza A1 - Li, Z. A1 - Zhang, Z. T1 - Grain boundary energy effect on grain boundary segregation in an equiatomic high-entropy alloy N2 - Grain boundary (GB) Segregation has a substantial effect on the microstructure evolution and properties of polycrystalline alloys. The mechanism of nanoscale segregation at the various GBs in multicomponent alloys is of great challenge to reveal and remains elusive so far. To address this issue, we studied the GB segregation in a representative equiatomic FeMnNiCoCr high-entropy alloy (HEA) aged at 450 °C. By combining transmission Kikuchi diffraction, atom probe tomography analysis and a density-based thermodynamics modeling, we uncover the nanoscale segregation behavior at a series of well-characterized GBs of different characters. No segregation occurs at coherent twin boundaries; only slight nanoscale segregation of Ni takes place at the low-angle GBs and vicinal \Sigma 29b coincidence site lattice GBs. Ni and Mn show cosegregation of high levels at the general high-angle GBs with a strong depletion in Fe, Cr, and Co. Our density-based thermodynamic model reveals that the highly negative energy of mixing Ni and Mn is the main driving force for nanoscale cosegregation to the GBs. This is further assisted by the opposite segregation of Ni and Cr atoms with a positive enthalpy of mixing. It is also found that GBs of higher interfacial energy, possessing lower atomic densities (higher disorder and free volume), show higher segregation levels. By clarifying the origins of GB segregations in the FeMnNiCoCr HEA, the current work provides fundamental ideas on nanoscale segregation at crystal defects in multicomponent alloys. KW - Thermodynamics KW - High-Entropy Alloys KW - Grain Boundary Segregation PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-508827 DO - https://doi.org/10.1103/PhysRevMaterials.4.053603 VL - 4 IS - 5 SP - 053603 PB - American Physical Society AN - OPUS4-50882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kindrachuk, Vitaliy A1 - Darvishi Kamachali, Reza T1 - Mean-field modeling and phase-field simulation of grain growth under directional driving forces N2 - Directional grain growth is a common phenomenon in the synthetic and natural evolution of various polycrystals. It occurs in the presence of an external driving force, such as a temperature gradient, along which grains show a preferred, yet competitive, growth. Novel additive manufacturing processes, with intense, localized energy deposition, are prominent examples of when directional grain growth can occur, beneath the melting pool. In this work, we derive a phenomenological mean-field model and perform 3D phase-field simulations to investigate the directional grain growth and its underlying physical mechanisms. The effect of the intensity of driving force is simulated and systematically analyzed at the evolving growth front as well as various cross-sections perpendicular to the direction of the driving force. We found that although the directional growth significantly deviates from normal grain growth, it is still governed by a power law relation α tⁿ with an exponent n ~ 0.6–0.7. The exponent n exhibits a nontrivial dependence on the magnitude of the directional driving force, such that the lowest growth exponent is observed for intermediate driving forces. We elaborate that this can originate from the fact that the forces at grain boundary junctions evolve out of balance under the influence of the directional driving force. With increasing the driving forces, the growth exponent asymptotically approaches a value of n ≈ 0.63, imposed by the largest possible grain aspect ratio for given grain boundary energies. The current combined mean-field and phase-field framework pave the way for future exploration in broader contexts such as the evolution of complex additively manufactured microstructures. KW - Additive manufacturing KW - Phase-field simulation KW - Grain growth KW - Mean-field modelling KW - Directional grain growth PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593210 DO - https://doi.org/10.1016/j.mtla.2023.101989 SN - 2589-1529 VL - 33 SP - 1 EP - 10 PB - Elsevier AN - OPUS4-59321 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -