skip to main content

Title: Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential

Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rankσSI,σIL, andσIL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in more » the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσSIandσSLalter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

« less
Award ID(s):
Publication Date:
Journal Name:
Materials Theory
Springer Science + Business Media
Sponsoring Org:
National Science Foundation
More Like this
  1. Clathrates of Tetrel elements (Si, Ge, Sn) have attracted interest for their potential use in batteries and other applications. Sodium-filled silicon clathrates are conventionally synthesized through thermal decomposition of the Zintl precursor Na4Si4, but phase selectivity of the product is often difficult to achieve. Herein, we report the selective formation of the type I clathrate Na8Si46using electrochemical oxidation at 450 °C and 550 °C. A two-electrode cell design inspired by high-temperature sodium-sulfur batteries is employed, using Na4Si4as working electrode, Naβ″-alumina solid electrolyte, and counter electrode consisting of molten Na or Sn. Galvanostatic intermittent titration is implemented to observe the oxidation characteristics and reveals a relatively constant cell potential under quasi-equilibrium conditions, indicating a two-phase reaction between Na4Si4and Na8Si46. We further demonstrate that the product selection and morphology can be altered by tuning the reaction temperature and Na vapor pressure. Room temperature lithiation of the synthesized Na8Si46is evaluated for the first time, showing similar electrochemical characteristics to those in the type II clathrate Na24Si136. The results show that solid-state electrochemical oxidation of Zintl phases at high temperatures can lead to opportunities for more controlled crystal growth and a deeper understanding of the formation processes of intermetallic clathrates.

  2. A method to predict sub-filter shear-induced velocities on a liquid-gas phase interface for use in a dual scale LES model is presented and compared against prior work on Vortex Sheet methods. The method reconstructs the sub-filter velocity field in the vicinity of the interface by employing a vortex sheet at the interface location. The vortex sheet is transported by an unsplit geometric volume and surface area advection scheme with a Piecewise Linear Interface Construction (PLIC) representation of the material interface. At each step, the vorticity field is constructed by evaluating a volume integral of the vortex sheet and a numerical spreading parameter near the liquid-gas interface. A Poisson equation can then be constructed and solved for the vector potential; the self-induced velocities due to the vortex sheet are subsequently evaluated from the vector potential. The described vortex sheet method is tested and compared against prior literature.
  3. Abstract

    Dynamic wetting phenomena are typically described by a constitutive law relating the dynamic contact angleθto contact-line velocityUCL. The so-called Davis–Hocking model is noteworthy for its simplicity and relatesθtoUCLthrough a contact-line mobility parameterM, which has historically been used as a fitting parameter for the particular solid–liquid–gas system. The recent experimental discovery of Xia & Steen (2018) has led to the first direct measurement ofMfor inertial-capillary motions. This opens up exciting possibilities for anticipating rapid wetting and dewetting behaviors, asMis believed to be a material parameter that can be measured in one context and successfully applied in another. Here, we investigate the extent to whichMis a material parameter through a combined experimental and numerical study of binary sessile drop coalescence. Experiments are performed using water droplets on multiple surfaces with varying wetting properties (static contact angle and hysteresis) and compared with numerical simulations that employ the Davis–Hocking condition with the mobilityMa fixed parameter, as measured by the cyclically dynamic contact angle goniometer, i.e. no fitting parameter. Side-view coalescence dynamics and time traces of the projected swept areas are used as metrics to compare experiments with numerical simulation. Our results show that the Davis–Hocking model with measured mobility parameter captures the essentialmore »coalescence dynamics and outperforms the widely used Kistler dynamic contact angle model in many cases. These observations provide insights in that the mobility is indeed a material parameter.

    « less
  4. Solid solutions of Mg 2 Si and Mg 2 Sn are promising thermoelectric materials owing to their high thermoelectric figures-of-merit and non-toxicity, but they may undergo phase separation under thermal cycling due to the presence of miscibility gaps, implying that the thermoelectric properties could be significantly degraded during thermoelectric device operation. Herein, this study investigates the strain-induced suppression of the miscibility gap in solid solutions of Mg 2 Si and Mg 2 Sn. Separately prepared Mg 2 Si and Mg 2 Sn powders were made into (Mg 2 Si) 0.7 (Mg 2 Sn) 0.3 mixtures using a high energy ball-milling method followed by spark plasma sintering. Afterwards, the phase evolution of the mixtures, depending on thermal annealing and mixing conditions, was studied experimentally and theoretically. Transmission electron microscopy and X-ray diffraction results show that, despite the presence of a miscibility gap in the pseudo-binary phase diagram, the initial mixture of Mg 2 Si and Mg 2 Sn evolved towards a solid solution state after annealing for 3 hours at 720 °C. Thermodynamic analysis as well as phase-field microstructure simulations show that the strain energy due to the coherent spinodal effect suppresses the chemical spinodal entirely and prevents phase separation. Thismore »strategy to suppress the miscibility gap induced by lattice strain through non-equilibrium processing can benefit the thermoelectric figure-of-merit by maximizing phonon alloy scattering. Furthermore, stable solid solutions by engineering phase diagrams have the potential to facilitate the reliable long term operation of thermoelectric generators under continuous thermal loads.« less
  5. Abstract

    The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified an additional stable Li3Sn phase with a large BCC-based hR48 structure and a possible high-TLiSn4ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, low-symmetry 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine-learning interatomic potentials hold for accelerating ab initio prediction of complex materials.