High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond II: Theory and Modeling I
Sponsored by: TMS Alloy Phases Committee, TMS Mechanical Behavior of Materials Committee
Program Organizers: Michael Gao, National Energy Technology Laboratory; Xingbo Liu, West Virginia University; Peter Liaw, University of Tennessee; Jian Luo, University of California, San Diego; Yiquan Wu, Alfred University; Yu Zhong, Worcester Polytechnic Institute; Mitra Taheri, Johns Hopkins University; Amy Clarke, Los Alamos National Laboratory
Wednesday 8:00 AM
October 20, 2021
Room: B132
Location: Greater Columbus Convention Center
Session Chair: Dilpuneet Aidhy, University of Wyoming; Yong-Jie Hu, Drexel University
8:00 AM Invited
Data-enabled Additive Manufacturing of High-entropy Alloys: Ganesh Balasubramanian1; Praveen Sreeramagiri1; 1Lehigh University
Additive manufacturing (AM) techniques, such as laser powder bed fusion (PBF) and direct energy deposition (DED), have emerged as attractive solutions for high-entropy alloy synthesis, albeit with major challenges of their own: (i) Poor fusion of the alloy powder and (ii) formation of cracks resulting from the uneven temperature distribution. In response to these challenges, we present results from a predictive framework correlating processing to structure and properties by coupling findings from computational materials simulations of the alloy melt with uncertainty quantification and experimental validation, geared towards proposing optimal manufacturing parameters to enhance the quality of the additively manufactured alloys.The research generates new knowledge on how diffusion of multiple elements in an alloy melt under rapid cooling contributes to the microstructure and properties of additively manufactured high temperature alloys. The key technology developed lies in a data-informed framework for manufacturing high-entropy materials to fabricate components relevant for on-demand applications.
8:30 AM Invited
Data-driven Design of Refractory High-entropy Alloys: George Kim1; Chanho Lee2; Peter Liaw3; Wei Chen1; 1Illinois Institute of Technology; 2Los Alamos National Lab; 3University of Tennessee
The material-design strategy of combining multiple elements in near-equimolar ratios has spearheaded the emergence of high-entropy alloys (HEAs), an exciting class of materials with exceptional engineering properties. While random mixing has been widely assumed in multi-principal element solid solutions, both experimental and computational evidence suggests short-range ordering (SRO) exists in many solid-solution HEAs. We employed an integrated first-principles and experimental approach to understanding the thermodynamic effects of SRO in the refractory NbTaTiV and NbTaTiVZr HEA systems. The existence of SRO produces distinct lattice distortion features in these HEAs and affects their mechanical properties. The fundamental understanding of SRO and lattice distortion is coupled with high-throughput first-principles calculations to design refractory high-entropy alloys.
8:50 AM
A Systematic Analysis of Phase Stability in Refractory High Entropy Alloys Utilizing Linear and Non-linear Cluster Expansion Models: Chiraag Nataraj1; Edgar Josué Landinez Borda2; Axel van de Walle1; Amit Samanta2; 1Brown University; 2Lawrence Livermore National Lab
The phase segregation behavior of three key refractory high entropy alloys (NbTiVZr, HfNbTaTiZr, and AlHfNbTaTiZr) is studied using first-principles calculations. Cluster expansion models are fitted for each alloy and utilized to generate Monte Carlo trajectories in order to explore the link between phase segregation and previously documented mechanical degradation in these materials. Phase segregation and intermetallic phases documented in the experimental literature are reproduced in all three high entropy alloys. NbTiVZr forms vanadium and zirconium clusters at lower temperatures (250 K) which disperse into the single-phase matrix by 1000 K. HfNbTaTiZr forms HfZr, NbTa, and possibly TiZr intermetallic phases at lower temperatures (250 K). Unlike the other HEAs studied here, HfNbTaTiZr does not lose short-range ordering in the solid state until around 3500 K, which is above its melting temperature. AlHfNbTaTiZr forms NbTa and AlHfTiZr phases at lower temperatures (250 K), which are not observed at higher temperatures (1000 K).
9:10 AM Invited
Atomic Transport by Point Defects and Clusters in Concentrated Alloys: Osetsky Yury1; Laurent Béland2; Alexander Barashev3; Yanwen Zhang4; 1Oak Ridge National Laboratory; 2Queen’s University; 3University of Michigan; 4University of Tennessee
Fascinating properties of many concentrated solid-solution alloys can be attributed to atomic-level diffusion and transport and the controlling mechanisms are complicated and depend very much on the alloy’s composition. Using microsecond-scale molecular dynamics, meso-scale kinetic Monte Carlo (kMC) and theory we have revealed the governing role of percolation effects and composition dependent defect formation and migration energies. An increase of concentration of faster species may decrease the overall atomic diffusion. Consequently, the composition dependence of diffusivity has a minimum near the corresponding mechanism percolation threshold and, we argue, may lead to the sluggish diffusion effect. Examples when additional element dramatically affects alloy diffusivity are given for fcc Ni-Fe-Cr system. A method for preselecting materials with potentially desired properties based on ab-initio informed kMC approach is suggested.This work was supported as part of the Energy Dissipation to Defect Evolution, an Energy Frontier Research Center funded by the US DOE/BES.
9:30 AM Invited
Joint Prediction of Mechanical Properties of Alloys with Enhanced Fidelity through Integration of Machine Learning (Data Analytics) and Multiscale Modeling: Baldur Steingrimsson1; Peter Liaw2; Jaafar El-Awady3; 1Imagars LLC; 2University of Tennessee; 3John Hopkins University
This presentation addresses the design of advanced alloys systems that can enable new propulsion systems for subsonic transport vehicles with high levels of thermal, transmission, and propulsive efficiency. Organizations, such as the National Aeronautics and Space Administration, are seeking integrated computational and experimental approaches that can decrease the time necessary for development, testing, and validation of such alloy systems and components.To this effect, we address the design and development of high-entropy alloys (HEAs) for subsonic transport vehicle propulsion system structures and components. We are looking to jointly optimize the mechanical properties of HEAs for applications involving turbine blades capable of operating at higher temperature and with greater efficiency, resulting in improved fuel efficiency and reduced emissions. Temperature and thermo-mechanical performance, environmental durability, reliability, and cost-effectiveness are here important considerations. We combine strengths of machine learning and multiscale (physics-based) modeling, for purpose of making up for limitations of each approach.
9:50 AM Invited
Predicting Fundamental Properties of BCC Refractory Multicomponent Alloys Using Electronic Descriptors and Statistical Learning: Yong-Jie Hu1; Christopher Tandoc1; Liang Qi2; 1Drexel University; 2University of Michigan
Optimizing chemistries of bcc refractory multicomponent alloys to achieve a synergy of high strength and low-temperature ductility requires reliable predictions of the correlated alloy properties across a vast compositional space. In this work, first-principles calculations were performed for 106 individual bcc solid-solution alloys to predict several strength/ductility-related fundamental alloy properties, including lattice distortions, unstable stacking fault energies, and surface energies. With the descriptors based on electronic structures of interatomic bonding, several statistical learning models were developed to efficiently and accurately predict the formation energies of these planar defects and magnitudes of lattice distortions according to alloying compositions. The developed statistical models further enabled rapid and systematic search of potential alloy candidates that are intrinsically ductile and with high yield strengths across high-order multicomponent systems.
10:20 AM Break
10:40 AM Invited
Machine Learning Enabled Defect Energies in Concentrated Alloys: Gaurav Arora1; Anus Manzoor1; Dilpuneet Aidhy1; 1University of Wyoming
Concentrated alloys, including high entropy alloys, consist of multiple principal elements randomly distributed on a crystal lattice that causes large variations in defect energies in a given alloy composition. Statistically capturing the variation requires performing large number of calculations which is computationally highly expensive. The challenge is compounded due to the exponentially large number of compositions that are possible in these alloys. We use machine learning approach to predict defect energies, where a database of simpler alloys is used to predict defect energies in complex alloys. We demonstrate predictions of vacancy formation and migration energies in five-element Ni-based alloys and stacking fault energies in ternary alloys. A benefit of this approach is that once the binary database is built and the model is trained, defect energies can be predicted with little computational expense thereby bypassing large number of calculations every time a new composition is discovered.
11:00 AM Invited
Now On-Demand Only - Determination of Fluctuations in Local Composition, Strain and Lattice Distortions in Multi-principal Component Alloys Using Advanced Transmission Electron Microscopy: Jian Min Zuo1; Haw-Wen Hsiao1; Yu-Tsun Shao1; Qun Yang1; Yang Hu1; 1University of Illinois
The concentrated solid solutions of multi-principal component alloys (MPEAs) present significant challenges for their characterization. While traditional characterization techniques have demonstrated their usefulness in determining bulk properties, however, the determination of local properties, such as fluctuations in local composition, short-range ordering, internal strain and stresses, and lattice distortions require sensitive high resolution probes as well as new methodologies in dealing with the complexities of randomness. Here we report on the combination of four-dimensional scanning transmission electron microscopy (4D-STEM) and high-resolution chemical analysis using STEM based X-ray energy-dispersive spectroscopy (EDS). Using this approach, we provide a detailed picture of compositional and structure fluctuations in MPEAs and discuss their role in strengthening and plasticity.