Advances in Multi-Principal Elements Alloys X: Structures and Modeling: Alloy Design and Modeling
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS: Alloy Phases Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Peter Liaw, University of Tennessee; Michael Gao, National Energy Technology Laboratory; E-Wen Huang, National Chiao Tung University; Jennifer Carter, Case Western Reserve University; Srivatsan Tirumalai; Xie Xie, FCA US LLC; Gongyao Wang, Alcoa Technical Center
Wednesday 2:00 PM
March 2, 2022
Room: 251B
Location: Anaheim Convention Center
Session Chair: E-Wen Huang, National Yang Ming Chiao Tung University; Saryu Fensin, Los Alamos National Laboratory
2:00 PM Invited
Data-driven Discovery of 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 HEAs cover a broad compositional space, the understanding of elemental combinations and their effects is still limited. We employed high-throughput first-principles calculations, machine learning, and association rule mining to uncover synergies of elements for HEA design. These computational results will be discussed with experimental validations in the space of refractory high-entropy alloys.
2:20 PM Invited
Predicting Fundamental Properties of Refractory Multicomponent Alloys Using Electronic Descriptors and Statistical Learning: Yong-Jie Hu1; Christopher Tandoc1; Liang Qi2; Peter Liaw3; 1Drexel University; 2University of Michigan; 3University of Tennessee
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 employed to predict several strength/ductility-related fundamental alloy properties, including lattice parameters, lattice distortions, unstable stacking fault energies, and surface energies, for 106 individual bcc refractory multicomponent alloys. With the calculation results and descriptors based on electronic structures of interatomic bonding, statistical learning models were developed to efficiently predict these fundamental alloy properties for arbitrary alloy compositions without the need of any further first-principles calculation. The developed statistical models enabled rapid and systematic search of potential alloy candidates that are intrinsically ductile and with high yield strengths in high-order multicomponent systems.
2:40 PM Invited
Development of Novel Refractory High-entropy Alloys via High-throughput Alloy-design Approach: Saryu Fensin1; chanho Lee1; James Valdez1; Nan Li1; 1Los Alamos National Laboratory
Refractory high-entropy alloys (RHEAs) have received great attention due to their high yield strength, and excellent resistance to softening at high-temperatures. However, there still exist vast unexplored regions of composition spaces within this HEA family that could lead to enhanced properties. Thus, to explore this space in an efficient manner, we propose to use a high throughput experimental manufacturing approach coupled with machine learning. In this work, we will present initial results from this approach based on the composition: Nb-Mo-Ta-Ti-V. The compositions of the RHEAs were changed one at a time in the thin films that were fabricated using physical vapor deposition (PVD). The morphology, structure, and chemical compositions of the designed RHEAs was characterized and the properties measured using nanoindentation techniques such as hardness and modulus. This data was then used as an input into a machine learning model to elucidate relationships between chemical composition and strength.
3:00 PM Invited
Magnetism in Metastable and Annealed HEAs of (FeNiCrMn): Nan Tang1; Lizabeth Quigley1; Walker Boldman1; Cameron Jorgensen1; Rémi Koch1; Daniel O'Leary1; Hugh Medal1; Philip Rack1; Dustin Gilbert1; 1University of Tennessee
Using by room-temperature combinatorial sputtering, metastable films of (Fe,Ni,Cr,Mn) were prepared. The mixed ferromagnetic/antiferromagnetic interactions in these alloys result in frustrated magnetic behavior which resolves below 50 K. At low temperatures, the coercivity achieves values of nearly 500 mT, which is comparable to some high-anisotropy magnetic materials. Commensurate with the divergent coercivity is an atypical drop in the temperature dependent magnetization. These effects are explained by a mixed magnetic phase model, consisting of ferro-, antiferro , and frustrated magnetic regions, and are rationalized by simulations. A machine-learning algorithm is employed to visualize the parameter space and inform the development of subsequent compositions. Annealing the samples at 600 °C orders the sample, more-than doubling the Curie temperature and increasing the saturation magnetization by as much as 5×. Simultaneously, the large coercivities are suppressed, resulting in magnetic behavior that is largely temperature independent over a range of 350 K.
3:20 PM Break
3:40 PM
Combination of High Throughput Experiments and ICME Approaches to Discover the Composition Space for Lightweight High Entropy Alloys: Shengyen Li1; John Macha1; Mirella Vargas1; Michael Miller1; 1Southwest Research Institute
This presentation will discuss the feasibility of integrating high throughput experiments (HTE) with computational approaches to discover the composition space for lightweight high entropy alloys (LHEAs). The objectives are to reduce density by 25% while the mechanical properties comparable to Ni-based superalloys at 1100 oC for high temperature applications. A combinatorial first principles approach initially screens out the design space by predicting the phase stability and intrinsic properties of multi-principal element alloys. To explore the potential space cost effectively, physical vapor deposition is used to synthesize compositional libraries, which are then characterized to assess microstructure features. A nanoindentation technique provides hardness and fracture toughness that, when combined with EDS mapping, informs alloy-structure-properties relationship. The results validate a mesoscale computational workflow, which uses CALPHAD-based models to predict the microstructure features for mechanistic models to estimate targeted properties, e.g. yield strength. The outcomes guide the iterative experiments to achieve the design goal.
4:00 PM Cancelled
Polymetallic MOF Derived High Entropy FeCoNiMnMo/NC Nanoparticles for Efficient Alkaline Hydrogen Evolution Reactions: Shiqi Wang1; Feng Fang1; 1Southeast University
Recently, high entropy alloys (HEAs) have been considered as new-generation catalysts in water electrolysis for their unique properties. However, the synthesis of well-designed HEAs catalyst remains a challenge. Herein, we present a facile strategy to synthesis advanced FeCoNiMnMo nanoparticles anchored on nitrogen-doped carbon (NC), derived from the polymetallic metal-organic framework (MOF). The optimized FeCoNiMnMo/NC achieve boosted Hydrogen Evolution Reaction (HER) performance in alkaline conditions (1 M KOH). The obtained overpotentials at 10 mA/cm2 is 41 mV with a low Tafel slope of 56.1 mV dec−1, even outperforming commercial Pt/C. Moreover, the new design showed exceptional long-term HER stability over 36 h testing. The density functional theory (DFT) calculations revealed that the synergistic effect of chemical complexity and abundant electrocatalytic sites could lower the energy barrier of water dissociation and promote the adsorption/desorption of reactive intermediates. This work verifies the usefulness and advantage of high-entropy materials for energy conversion and storage.
4:20 PM
Multiscale Modeling and Design of High Entropy Alloys: Justin Almeida1; Jide Oyerinde1; Philip Yuya1; Ioannis Mastorakos1; 1Clarkson University
We present a methodology of designing high entropy alloys based on continuum dislocation dynamics multiscale modeling. The developed framework combines Fast Fourier Transform (FFT) and Continuum Dislocation Dynamics (CDD) to model the evolution of dislocation density in polycrystalline high entropy alloys. Experimental results are used to evaluate the critical model parameters and identify empirical relations between these parameters and important compositional related physical properties. These empirical models can be used in the design of new high entropy alloys with tailored mechanical properties.