Effects of Annealing on Microstructure and Mechanical Properties of Metastable Powder Metallurgy CoCrFeNiMo0.2 High Entropy Alloy

Zhang, Cui; Liu, Bin; Liu, Yong; Fang, Qihong; Guo, Wenmin; Hu, Yang

doi:10.3390/e21050448

Cited by 19 publications

(3 citation statements)

References 29 publications

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“…We collected a total of 29 unique FCC-based HEAs, whose as-cast yield strength data are experimentally measured at room temperature. [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] Not all compositions have equiatomic concentrations of constituent elements. In addition, experimentally measured room temperature elastic constants data do not exist for the compositions in Data set 2.…”

Section: A Data Setsmentioning

confidence: 99%

Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys

Liu,

Lee,

Balachandran

2022

Preprint

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Accelerating the design of materials with targeted properties is one of the key materials informatics tasks. The most common approach takes a data-driven motivation, where the underlying knowledge is incorporated in the form of domain-inspired input features. Machine learning (ML) models are then built to establish the input-output relationships. An alternative approach involves leveraging mechanistic models, where the domain knowledge is incorporated in a predefined functional form.These mechanistic models are meticulously formulated through observations to validate specific hypotheses, and incorporate elements of causality missing from data-driven ML approaches. In this work, we demonstrate a computational approach that integrates mechanistic models with phenomenological and ML models to rapidly predict the temperature-dependent yield strength of high entropy alloys (HEAs) that form in the single-phase face-centered cubic (FCC) structure. Our main contribution is in establishing a quantitative relationship between the HEA compositions and temperature-dependent elastic constants. This allows us to improve the treatment of elastic constant mismatch to the solid solution strengthening effect in the mechanistic model, which is important for reliable prediction of yield strength as a function of temperature in single-phase FCC-based HEAs.We accomplish this by combining Bayesian inference with ensemble ML methods. The outcome is a probability distribution of elastic constants which, when propagated through the mechanistic model, yields a prediction of temperature-dependent yield strength, along with the uncertainties.The predicted yield strength shows good agreement with published experimental data, giving us confidence in applying the developed approach for the rapid search of novel FCC-based HEAs with excellent yield strength at various temperatures.

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Section: A Data Setsmentioning

confidence: 99%

Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys

Liu,

Lee,

Balachandran

2022

Preprint

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“…Annealing—common in metallurgy—heats a metal or alloy to a set temperature, holds this temperature, and then cools to room temperature to improve the physical and sometimes also the chemical properties of the material (1, 2). During annealing, recrystallization repairs defects to afford a refined microstructure, which can relieve stress, soften the metal, increase the ductility, and improve the grain structure.…”

Section: Introductionmentioning

confidence: 99%

Annealing synchronizes the 70S ribosome into a minimum-energy conformation

Chu

et al. 2021

Preprint

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Researchers commonly anneal metals, alloys, and semiconductors to repair defects and improve microstructures via recrystallization. Theoretical studies indicate simulated annealing on biological macromolecules helps predict the final structures with minimum free energy. Experimental validation of this homogenizing effect and further exploration of its applications are fascinating scientific questions that remain elusive. Here, we chose the apo-state 70S ribosome from Escherichia coli as a model, wherein the 30S subunit undergoes a thermally driven inter-subunit rotation and exhibits substantial structural flexibility as well as distinct free energy. We experimentally demonstrate that annealing at a fast cooling rate enhances the 70S ribosome homogeneity and improves local resolution on the 30S subunit. After annealing, the 70S ribosome is in a nonrotated state with respect to corresponding intermediate structures in unannealed or heated ribosomes, and exhibits a minimum energy in the free energy landscape. One can readily crystallize these minimum-energy ribosomes, which have great potential for synchronizing proteins on a single-molecule level. Our experimental results are consistent with theoretical analysis on the temperature-dependent Boltzmann distribution, and offer a facile yet robust approach to enhance protein stability, which is ideal for high-resolution cryogenic electron microscopy. Beyond structure determination, annealing can be extended to study protein folding and explore conformational and energy landscape.

show abstract

“…Annealing—common in metallurgy—heats a metal or alloy to a set temperature, holds this temperature, and then cools the metal to room temperature to improve the physical and sometimes also the chemical properties of the material ( 1 , 2 ). During annealing, recrystallization repairs defects to afford a refined microstructure, which can relieve stress, soften the metal, increase the ductility, and improve the grain structure.…”

mentioning

confidence: 99%

Annealing synchronizes the 70 S ribosome into a minimum-energy conformation

Chu

Liu

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Researchers commonly anneal metals, alloys, and semiconductors to repair defects and improve microstructures via recrystallization. Theoretical studies indicate that simulated annealing on biological macromolecules helps predict the final structures with minimum free energy. Experimental validation of this homogenizing effect and further exploration of its applications are fascinating scientific questions that remain elusive. Here, we chose the apo-state 70S ribosome from Escherichia coli as a model, wherein the 30S subunit undergoes a thermally driven intersubunit rotation and exhibits substantial structural flexibility as well as distinct free energy. We experimentally demonstrate that annealing at a fast cooling rate enhances the 70S ribosome homogeneity and improves local resolution on the 30S subunit. After annealing, the 70S ribosome is in a nonrotated state with respect to corresponding intermediate structures in unannealed or heated ribosomes. Manifold-based analysis further indicates that the annealed 70S ribosome takes a narrow conformational distribution and exhibits a minimum-energy state in the free-energy landscape. Our experimental results offer a facile yet robust approach to enhance protein stability, which is ideal for high-resolution cryogenic electron microscopy. Beyond structure determination, annealing shows great potential for synchronizing proteins on a single-molecule level and can be extended to study protein folding and explore conformational and energy landscapes.

show abstract

Effects of Annealing on Microstructure and Mechanical Properties of Metastable Powder Metallurgy CoCrFeNiMo0.2 High Entropy Alloy

Cited by 19 publications

References 29 publications

Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys

Integrating Machine Learning with Mechanistic Models for Predicting the Yield Strength of High Entropy Alloys

Annealing synchronizes the 70S ribosome into a minimum-energy conformation

Annealing synchronizes the 70 S ribosome into a minimum-energy conformation

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