Linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field design and machine learning

AbstractThe optimal design of shape memory alloys (SMAs) with specific properties is crucial for the innovative application in advanced technologies. Herein, inspired by the recently proposed design concept of concentration modulation, we explore martensitic transformation (MT) in and design the mechanical properties of Ti-Nb nanocomposites by combining high-throughput phase-field simulations and machine learning (ML) approaches. Systematic phase-field simulations generate data of the mechanical properties for various nanocomposites constructed by four macroscopic degrees of freedom. An ML-assisted strategy is adopted to perform multiobjective optimization of the mechanical properties, through which promising nanocomposite configurations are prescreened for the next set of phase-field simulations. The ML-guided simulations discover an optimized nanocomposite, composed of Nb-rich matrix and Nb-lean nanofillers, that exhibits a combination of mechanical properties, including ultralow modulus, linear super-elasticity, and near-hysteresis-free in a loading-unloading cycle. The exceptional mechanical properties in the nanocomposite originate from optimized continuous MT rather than a sharp first-order transition, which is common in typical SMAs. This work demonstrates the great potential of ML-guided phase-field simulations in the design of advanced materials with extraordinary properties.

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Ferroic Multipolar Order and Disorder in Cyanoelpasolite Molecular Perovskites

10.26434/chemrxiv.7358363.v1 ◽

2018 ◽

Author(s):

Chloe Coates ◽

Harry Gray ◽

Johnathan Bulled ◽

Hanna Boström ◽

Arkadiy Simonov ◽

...

Keyword(s):

Potts Model ◽

Degrees Of Freedom ◽

Variable Temperature ◽

First Order ◽

Order And Disorder ◽

Simple Cubic ◽

Different Types ◽

Mechanical Models ◽

Statistical Mechanical ◽

First Order Transition

<div>We use a combination of variable-temperature high-resolution synchrotron X-ray powder diffraction measurements and Monte Carlo simulations to characterise the evolution of two different types of ferroic multipolar order in a series of cyano elpasolite molecular perovskites. We show that ferroquadrupolar order in [C3N2H5]2Rb[Co(CN)6] is a first-order process that is well described by a 4-state Potts model on the simple cubic lattice. Likewise, ferrooctupolar order in [NMe4]2B[Co(CN)6] (B = K, Rb, Cs) also emerges via a first-order transition that now corresponds to a 6-state Potts model. Hence, for these particular cases, the dominant symmetry breaking mechanisms are well understood in terms of simple statistical mechanical models. By varying composition, we find that the effective coupling between multipolar degrees of freedom—and hence the temperature at which ferromultipolar order emerges—can be tuned in a chemically sensible manner.</div><div><br></div>

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Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computation

Carbon ◽

10.1016/j.carbon.2019.03.046 ◽

2019 ◽

Vol 148 ◽

pp. 115-123 ◽

Cited By ~ 9

Author(s):

Zesheng Zhang ◽

Yang Hong ◽

Bo Hou ◽

Zhongtao Zhang ◽

Mehrdad Negahban ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Throughput

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Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential

Materials Theory ◽

10.1186/s41313-021-00038-0 ◽

2022 ◽

Vol 6 (1) ◽

Author(s):

Vahid Attari ◽

Raymundo Arroyave

Keyword(s):

Machine Learning ◽

High Throughput ◽

Phase Field ◽

Interface Energy ◽

Energy Space ◽

Supervised Machine Learning ◽

Phase Field Method ◽

Phase Field Modeling ◽

Field Modeling ◽

Multi Phase

AbstractComputational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5 intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank σSI,σIL, and σIL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that shows σSI and σSL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

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Design of comprehensive mechanical properties by machine learning and high-throughput optimization algorithm in RAFM steels

Nuclear Engineering and Technology ◽

10.1016/j.net.2019.10.014 ◽

2020 ◽

Vol 52 (5) ◽

pp. 1008-1012 ◽

Cited By ~ 1

Author(s):

Chenchong Wang ◽

Chunguang Shen ◽

Xiaojie Huo ◽

Chi Zhang ◽

Wei Xu

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Throughput ◽

Optimization Algorithm ◽

Throughput Optimization

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Ferroic multipolar order and disorder in cyanoelpasolite molecular perovskites

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2018.0219 ◽

2019 ◽

Vol 377 (2149) ◽

pp. 20180219 ◽

Cited By ~ 5

Author(s):

C. S. Coates ◽

H. J. Gray ◽

J. M. Bulled ◽

H. L. B. Boström ◽

A. Simonov ◽

...

Keyword(s):

Potts Model ◽

Degrees Of Freedom ◽

Variable Temperature ◽

First Order ◽

Order And Disorder ◽

Simple Cubic ◽

Different Types ◽

Mechanical Models ◽

Statistical Mechanical ◽

First Order Transition

We use a combination of variable-temperature high-resolution synchrotron X-ray powder diffraction measurements and Monte Carlo simulations to characterize the evolution of two different types of ferroic multipolar order in a series of cyanoelpasolite molecular perovskites. We show that ferroquadrupolar order in [C 3 N 2 H 5 ] 2 Rb[Co(CN) 6 ] is a first-order process that is well described by a four-state Potts model on the simple cubic lattice. Likewise, ferrooctupolar order in [NMe 4 ] 2 B[Co(CN) 6 ] (B = K, Rb, Cs) also emerges via a first-order transition that now corresponds to a six-state Potts model. Hence, for these particular cases, the dominant symmetry breaking mechanisms are well understood in terms of simple statistical mechanical models. By varying composition, we find that the effective coupling between multipolar degrees of freedom—and hence the temperature at which ferromultipolar order emerges—can be tuned in a chemically sensible manner. This article is part of the theme issue ‘Mineralomimesis: natural and synthetic frameworks in science and technology’.

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Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials

10.21203/rs.3.rs-902313/v1 ◽

2021 ◽

Author(s):

Chinedu Ekuma ◽

Z Liu ◽

Srihari Kastuar

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Throughput ◽

Learning Algorithm ◽

2D Materials ◽

Machine Learning Algorithm ◽

New Materials ◽

Temperature Dependent ◽

Efficient Prediction ◽

Properties Of Materials

Abstract An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures, including their temperature-dependent mechanical properties and developed a machine learning algorithm for exploring predicted properties.

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