High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond III: Theory and Modeling
Sponsored by: TMS: Nanomaterials Committee
Program Organizers: Yu Zhong, Worcester Polytechnic Institute; 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; Mitra Taheri, Johns Hopkins University; Amy Clarke, Los Alamos National Laboratory
Monday 2:00 PM
October 10, 2022
Room: 324
Location: David L. Lawrence Convention Center
Session Chair: Songge Yang, Worcester Polytechnic Institute; Guangchen Liu, Worcester Polytechnic Institute
2:00 PM Invited
Modeling of Oxidation Resistance in Ni-containing High Entropy Alloys: A Combined First-principles and Machine Learning Study: Shun-Li Shang1; Yi Wang1; Zi-Kui Liu1; Michael Gao2; 1Pennsylvania State University; 2National Energy Technology Laboratory
Oxidation involves diffusion of oxygen and formation of oxides. These processes could lead to severe degradation of materials performance and hence appeal for understanding and predictive modeling of oxidation resistance through thermodynamics and kinetics. Taking Ni-containing high entropy alloys (HEAs) as an example, we examine the predicted diffusivity and the underlaying physics of atoms (especially oxygen) in nickel affected by alloying elements in terms of a combined transition state energetics based on first-principles calculations and machine learning analyses. In addition, the CALPHAD modeling approach is used to predict a compositional feasibility map to identify the formation of dense oxides (Al2O3 and Cr2O3) and other oxides. Knowledge of diffusion and feasibility map makes it possible to understand, predict, and optimize oxidation resistance as demonstrated in Ni-containing HEAs.
2:20 PM
Revisit the VEC Criterion with High-throughput Ab Initio Calculations: A Case Study With Al-Co-Cr-Fe-Ni System: Songge Yang1; Guangchen Liu1; Yu Zhong1; 1Worcester Polytechnic Institute
Valence electron concentration (VEC) was treated as a valuable parameter to predict the stability of solid solution phases. However, the available experimental data to support this criterion is far from enough. In the current study, the high-throughput ab initio modeling is applied to investigate the relative stability of FCC and BCC Al-Co-Cr-Fe-Ni HEA single crystals by using the special quasi-random structure (SQS) approach. The predictions will start with pure elements of the Al-Co-Cr-Fe-Ni system and will be continued with binaries, ternaries, quaternary, and quinary compositions. More than 180 compositions were simulated. After that, the VEC criterion's reliability will be testified start from pure elements to the quinary alloys.
2:40 PM
An Automated, Machine Learning-driven Framework for Predicting High Temperature Oxidation Properties in Refractory Complex, Concentrated Alloys: Sharmila Karumuri1; Saswat Mishra1; Vincent Mika1; Collin Scott1; Nimish Awalgaonkar1; Austin Hernandez1; Kenneth Sandhage1; Ilias Bilionis1; Alejandro Strachan1; Michael Titus1; 1Purdue University
Refractory complex, concentrated alloys (RCCAs) offer new avenues for designing high strength and oxidation resistant materials at elevated temperature. However, RCCAs often exhibit multiple oxidation mechanisms including oxide volatilization, internal oxidation, external scale formation, and pesting. These overlapping and complex mechanisms has impeded efforts to predict oxidation behavior based on alloy compositions. In this presentation, we will present our recent efforts to create an open-source, automated framework housed in NanoHub for experimental data ingestion and storage of high temperature oxidation experimental data. During data ingestion, this framework automatically fits oxidation mass gain curves and extracts oxidation model parameters with quantified uncertainty. So far, more than 100 unique compositions and 380 unique experimental mass gain curves have been collected and analyzed. Predictions of mass gain behavior of RCCAs utilizing machine learning with physics-based descriptors will be presented, and recent efforts to design new RCCAs with superior oxidation resistance will be shown.
3:00 PM
First Principles Prediction of Mechanical Properties of High Entropy Alloys: Guofeng Wang1; Siming Zhang1; 1University of Pittsburgh
To enable rational design of high entropy alloys (HEAs), we have developed a first principles density functional theory (DFT) computational approach to predict the mechanical properties of HEAs. Specifically, we used the DFT method to calculate the lattice constants, elastic constants, and stacking fault energy for select HEAs with face-centered cubic lattice structure and varying chemical composition. The predicted mechanical properties include Young’s modulus, shear modulus, and yield strength of the HEAs. We have examined our computational approach for four alloy systems, i.e., NiCoFe, CoCrFeNi, CoCrFeCuNi, and RdIrPdPtNiCu HEAs. For these four HEAs with dramatically different chemical composition, our predicted mechanical properties are found to agree well experimental values. Consequently, we have demonstrated that the developed first principles based computational approach is a reliable computational tool for understanding the composition-structure-property relation of HEAs and, particularly, exploring novel HEAs with superior mechanical properties over vast composition space.
3:20 PM Break
3:40 PM Cancelled
Machine Learning Guided High Entropy Alloy Discovery: John Sharon1; Ken Smith1; Soumalya Sarkar1; Ryan Deacon1; Anthony Ventura1; GV Srinivasan1; 1Raytheon Technologies Research Center
High Entropy Alloys (HEAs) with multiple principal elements have demonstrated enhanced properties that can rival or exceed conventional alloy systems. HEAs are typically comprised of 4 or more elements present from 5 to 35 at.% resulting in a large combinatorial composition space for which computational tools are vital to sort through combinations and identify the most promising candidates. A variety of analytical and other relatively fast computational models are available to help identify candidates. We will describe a machine learning framework assembled to assist in identifying candidates that leverages experimental data, published literature, as well as physics-based models. By incorporating a combination of objectives and constraints, this machine learning approach enables us to set initial criteria and identify promising composition families based on targeted component performance metrics. Examples of using the framework to identify potential new HEA candidates will be discussed with complementary experimental characterization and validation.
4:00 PM
High-entropy Materials Design by Integrating the First-principles Calculations and Machine Learning: A Case Study in the Al-Co-Cr-Fe-Ni System: Guangchen Liu1; Songge Yang1; Yu Zhong1; 1Worcester Polytechnic Institute
The first-principles calculation is widely used in high entropy materials. However, this approach may consume many computational resources for complex systems, limiting the construction of property maps for the corresponding materials over a full composition range. In this work, the most common Al-Co-Cr-Fe-Ni system (both fcc and bcc) is selected for our investigation. We formulate a materials design strategy that combines first-principles calculation results and machine learning models to establish a robust database of properties (e.g., phase stabilities and elastic constants): starting from unary, binary, ternary, and quaternary, then extending into high-order systems. Moreover, analyzing and screening this database can further inspire discovering and designing new high entropy materials.
4:20 PM
Mechanical Properties And Deformation Mechanisms In TiMoNbZr Medium Entropy Alloys: A Molecular Dynamics Study: Avinash Chavan1; Mangal Roy1; 1IIT Kharagpur
The concept of refractory multicomponent system provides an interesting approach in designing novel metallic biomaterial. Multicomponent system consisting of biocompatible elements targeting biomedical applications. In the present work an atomic model of equiatomic TiMoNbZr medium entropy alloy was built using a melting and quick quenching method and mechanical behaviour of the equiatomic alloy under uniaxial compression and tensile loading are further studied using atomistic simulation. The plastic deformation of TiMoNbZr is affected by the motion of dislocation loops. The prismatic dislocation loops inside TiMoNbZr are formed by the dislocations with the Burgers vectors of a/2 [1 1 1]. In addition, elastic constants were calculated at 0 K for bcc TiMoNbZr alloy and validated with DFT results. Results illustrate a superior metallic biomaterial that possess a desirable combination of high strength and low modulus for the application of biomaterials.
4:40 PM
Phonon Broadening and Thermal Conductivity in High Entropy Ceramic Carbide: Linu Malakkal1; Kaustubh Bhawane1; Cody Dennett1; Zilong Hua1; Lingfeng He1; Yongfeng Lu2; Bai Cui2; 1Idaho National Lab; 2University of Nebraska
In the quest for novel irradiation-resistant and high-temperature tolerant ceramic materials for nuclear applications, the high entropy ceramic carbide materials with outstanding properties are a promising material class. At high temperatures, material properties such as thermal stability, thermodynamic, elastic property, and thermal conductivity are critically influenced by atomic vibrations (phonons). In a crystalline ceramic, carbides with an extreme disorder of cations can induce significant phonon scattering and broadening, owing to the inherently present mass and force constant variances. Despite the importance, phonon scattering and broadening in high entropy ceramic carbide are still lacking. Hence, in this work, we use the abinitio calculations to systematically investigate the impact of the mass and force constant variance on the phonon spectral function of face-centered cubic HECC, from binaries up to 5-component high entropy alloys, addressing the key question of how chemical complexity impacts the phonons.