Environmental Degradation of Multiple Principal Component Materials: Design, Modeling, Simulation, and Machine Learning
Sponsored by: TMS Structural Materials Division, TMS: Corrosion and Environmental Effects Committee, TMS: Nuclear Materials Committee
Program Organizers: Wenjun Cai, Virginia Polytechnic Institute and State University; ShinYoung Kang, Lawrence Livermore National Laboratory; XiaoXiang Yu, Novelis Global Research Center; Vilupanur Ravi, California State Polytechnic University Pomona; Christopher Weinberger, Colorado State University; Elizabeth Opila, University of Virginia; Bai Cui, University of Nebraska-Lincoln; Mark Weaver, University of Alabama; Bronislava Gorr, Karlsruhe Institute of Technology (KIT); Srujan Rokkam, Advanced Cooling Technologies, Inc.

Wednesday 8:30 AM
March 2, 2022
Room: 201C
Location: Anaheim Convention Center

Session Chair: Xiao-xiang Yu, Northwestern University ; Shinyoung Kang, Lawrence Livermore National Laboratory


8:30 AM  
Modeling Preferential Dissolution during Aqeous Corrosion of Multi-principal Element Alloys: Kang Wang1; Bi-Cheng Zhou1; 1University of Virginia
    Aqueous corrosion consists of intertwined processes of electrochemical/chemical reactions at anode and cathode, mass/charge transport in aqueous solution, and migration of metal/electrolyte interface. Previous modeling efforts mostly focus on the overall corrosion rate and morphological evolution of the metal/electrolyte interface, while the role of individual alloy elements leading to the preferential dissolution of multi-component alloys is rarely considered. Here, we apply the principles of non-equilibrium thermodynamics to model the kinetics at the metal/electrolyte interface and incorporate the multiple internal dissipation processes of alloy elements by thermodynamic variation of entropy functional. Coupled with CALPHAD-type thermodynamic and kinetic databases, the current work is applied to Ni38Fe20Cr22Mn10Co10 alloy to analyze the preferential dissolution and the corresponding predominant reaction mechanisms. With enhanced capability for multi-component alloys, the current work brings corrosion theories primarily for simple metals (e.g., pure metals and binary alloys) closer to real-life applications for commercial alloys in aqueous environments.

8:50 AM  
NOW ON-DEMAND ONLY – Interpretable Machine Learning to Understand Corrosion in Complex Compositional Alloys: Timothy Hartnett1; Angela Gerard1; Prasanna Balachandran1; John Scully1; 1University Of Virginia
    Machine learning (ML) is rapidly becoming an important computational tool for exploring the structure-property relationships in complex compositional alloys (CCAs), including the high entropy alloys (HEAs). When studying the corrosive behavior of these materials, variability in processing and characterization leads to heterogenous datasets that adds an additional layer of complexity to training ML models. In addition to demonstrating the generalizability of machine learning models, it is critical to probe the learned models to glean insights into the source of model predictions. Here we use novel local interpretability techniques to explore the behavior of ML models trained to predict the passivation current density of CCAs. These techniques offer a detailed look into how a model thinks each training feature impacts the passivation current density. The results offer a new approach for understanding the complex behavior of heterogeneous systems using ML.

9:10 AM  Invited
Corrosion Resistance of Al-Cr-Ti Containing Compositionally Complex Alloys: Samuel Inman1; Jie Qi2; Mark Wischhusen1; Sean Agnew1; Joseph Poon2; John Scully1; 1Department of Materials Science and Engineering- University of Virginia; 2Department of Physics- University of Virginia
    We describe the design of lightweight, low-cost compositionally complex alloys with good combinations of strength, ductility, and corrosion resistance. Iron containing FCC alloys were investigated and compared to corrosion resistant commercial marine alloys. Alloy compositions selected with high-throughput machine learning based methods were experimentally verified to possess FCC or FCC with B2 phases in the annealed state. Corrosion resistance equal to and, in some cases, superior to 316L stainless steel was accounted for by Al-Ti-Cr containing oxides. Corrosion resistance depends on balancing the composition of each phase to enable natural passivation and minimize detrimental solute depletion. Although alloying with Al and Ti decreases density, it can introduce phase segregation that may be detrimental to corrosion resistance.

9:40 AM  
Computational Investigation of the Trends that Govern the Coefficient of Thermal Expansion in Rare-earth Silicates: Mukil Ayyasamy1; 1University Of Virginia
    In applications such as aviation jet engines, where temperatures as high as 1300C are common in the hottest sections, environmental barrier coatings (EBCs) protect the Si-based ceramic matrix composites (CMCs) gas turbine components from reacting with corrosive salts and water vapor at extreme conditions. The objective of this work is to develop an understanding of the trends that govern the coefficient of thermal expansion (CTE) in rare-earth monosilcates (RE2SiO5) and rare-earth disilicates (RE2Si2O7), which are two key EBC candidates for high temperature applications. In select members of the RE2SiO5 family, our density functional theory (DFT) calculations reveal a correlation between the observed CTE anisotropy and electronic structure. In addition, our DFT work on the polymorphs of a high-entropic RE2Si2O7 system capture the complexities in the local RE-O and Si-O bond geometries. The outcome of this work has major implications in developing design rules for the rational design of EBC materials.

10:00 AM Break

10:20 AM  
Oxygen Modulation of Miscibility and Ordering in BCC Nb-Ti-Zr Alloys: Michael Waters1; David Beaudry2; Yevgeny Shlafstein2; Elaf Anber2; Mitra Taheri2; James Rondinelli1; 1Northwestern University; 2Johns Hopkins University
    We use the Nb-Ti-Zr composition space as an example system to explore how internal oxidation of a MPEA can affect the equilibrium phase diagram and ordering. Starting with density functional theory calculations as training data, we construct light-weight surrogate models in the form of cluster expansions and machine-learned interatomic potentials. With these models, we compute the equilibrium phase diagram and perform molecular dynamics simulations in a synergistic approach to compare to experimental measurements of short-range order. The Nb-Zr miscibility gap is specifically studied and the ternary BCC phase diagrams are reported as a function of increasing oxygen interstitial content.

10:40 AM  
Machine Learning Potential for High Entropy Alloys: Qiang Zhu1; Yanxon Howard1; Pedro Santos1; Xiaoxiang Yu2; Yunjiang Wang3; 1University of Nevada Las Vegas; 2Northwestern University; 3State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences
    In the past, the simulation of high entropy alloys (HEA) was largely based on the classical interatomic potentials. In this talk, we present PyXtal_FF—a package based on Python programming language—for developing machine learning potentials (MLPs) for more accurate modeling that is close to the level quantum mechanic simulation. The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. In particular, PyXtal_FF can train MLPs with neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from ab-initio simulations. The trained MLP model from PyXtal_FF can be interfaced with the ASE and LAMMPS packages for large scale atomistic simulations. We will illustrate the performance of PyXtal_FF by applying it to investigate the BCC NbMoTaW, as well as several other prototypical binary systems.

11:00 AM  
Modeling and Design of CoCrFeNi Multi-principle Element Alloys on Their Aqueous Corrosion Resistance via First Principle Calculations: Zhengyu Zhang1; Liping Liu1; Tianyou Mou1; Hongliang Xin1; Wenjun Cai1; 1Virginia Polytechnic Institute and State University
    Multi-principle element alloys (MPEAs) such as equiatomic CoCrFeNi have been studied extensively for their outstanding mechanical and corrosion properties, but little is known about their properties if the composition is off-equiatomic. In this work, we proposed a computational method as a first step in ferromagnetic multi-principle element alloy design to simulate MPEAs with different Cr contents; the corrosion and mechanical properties of CoCrFeNi MPEAs was studied by first principle method. The effects of Cr content on bulk modulus, electronic work function and anisotropy of CoCrFeNi MPEAs were discussed. Our simulation results revealed that CoCrFeNi with higher Cr content exhibit better corrosion resistance compared with equiatomic CoCrFeNi or their lower Cr counterparts. In addition, crystallographic orientation was also found to play a significant role affecting surface properties of MPEAs. These discoveries may shed light on the future design principles of off-equiatomic MPEAs for enhanced surface properties.