Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Zhang, Hongtao; Fu, Huadong; He, Xingqun; Wang, Changsheng; Jiang, Lei; Chen, Lei; Xie, Jun

doi:10.1016/j.actamat.2020.09.068

Cited by 151 publications

(49 citation statements)

References 37 publications

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“…[41][42][43] The eleven atomic properties can be classified into two categories (with values listed in Table S1, Supporting Information): atomic dimension and structure (atomic mass M, atomic radius r M , d-electron count θ d , s-electron count θ s , outermost electron number N e , atomic number in the periodic table of elements N atom , and atomic group number G) and reactivities (describing adsorption properties ϕ ad , electronegativity χ, electron affinity E A and first ionization energy E i ). Next, recursive elimination and exhaustive screening [44] are employed to identify key atomic properties (see Figure S2, Supporting Information). The key atomic properties, as listed in Table S2 (Supporting Information), affect the stability and OER/ORR electrocatalytic performance of DACs most significantly.…”

Section: Topological Information-based ML Algorithmmentioning

confidence: 99%

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Guo

2022

Adv Funct Materials

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Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.

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Section: Topological Information-based ML Algorithmmentioning

confidence: 99%

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Guo

2022

Adv Funct Materials

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“…Thus, the welding or joining of Ti 3 SiC 2 ceramic and metals is a problem we have to solve. Due to the outstanding conductivity, high fracture toughness and good workability, 7–9 pure copper is widely used in electrically conductive components. When Ti 3 SiC 2 ceramic and pure copper need to be welded in industrial applications, such as the manufacture of pantograph for high‐speed train, brazing is one of the most mature and reliable welding methods for ceramics and metals 5 .…”

Section: Introductionmentioning

confidence: 99%

Improvement for Ti₃SiC₂/Cu joint brazed using composite fillers with abnormal expansion ceramic particulates

et al. 2022

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Reducing the residual stresses and improving the mechanical strength of largescale ceramic/metal brazing joints is an important problem that must be solved for its practical engineering application. Using composite filler with solid-state phase transformation ceramic particulates, it is theoretically feasible to relieve the residual stress and improve the mechanical properties of ceramic/metal brazed joints. In this study, Cu mesh, Ag-28Cu-2Ti (wt.%), and yttria-stabilized zirconia (0.6 mol.% YSZ solid-state phase transformation ceramic particulates) composite power fillers were used in the brazing of Ti 3 SiC 2 ceramic and pure copper. The microstructure of joints and YSZ particulates in the interface was investigated and confirmed by scanning electron microscopy (SEM), energydispersive X-ray spectroscopy (EDS), scanning transmission electron microscopy (STEM), and transmission electron microscopy (TEM). In addition, the effect of YSZ particulates content on the mechanical properties of joints was investigated and evaluated by the shear strength. The results show that the interfacial phases were mainly Ti 5 Si 3 , TiC, Ti x Cu, Ag (s, s), Cu (s, s), and YSZ particulates. Moreover, most of YSZ particulates undergo the solid-state phase transformation from tetragonal zirconia (t-ZrO 2 ) to monoclinic zirconia (m-ZrO 2 ) during the cooling process of brazing. The abnormal volume expansion of the solid-state phase transformation reduced the thermal mismatch between Ti 3 SiC 2 ceramic and filler, thereby reducing the residual stress in the interface of joint. When using composite filler with 6 wt.% YSZ particulates, the shear strength of Ti 3 SiC 2 /Cu joint reached the maximum. The maximum average shear strength of the joints was 80.2 MPa, which was about 103.6% more than the joint without YSZ particulates.

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“…Machine learning (ML) approaches have been transforming materials research by changing the paradigm from "trial-and-error" to a data-driven methodology, especially in high-entropy alloys (Wen et al, 2019;Zhang et al, 2020aZhang et al, , 2020b, perovskite catalysts (Weng et al, 2020), shape memory alloys (Xue et al, 2016) and copper alloys (Zhang et al, 2020a(Zhang et al, , 2020b(Zhang et al, , 2021Wang et al, 2019). Corrosion behavior research has also begun to focus on the ML prediction for corrosion rate of low-alloy steel (Yan et al, 2020;Diao et al, 2021) and carbon steel (Zhi et al, 2021;Pei et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning

Jiang

Yan

2022

ACMM

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Purpose Cobalt-based alloys exhibit a unique combination of wear resistance, strength and corrosion resistance. Localized corrosion of such alloys in seawater system can be several orders of magnitude faster than general corrosion, and direct experimental evidence of the local activation process is still lacking, which makes the accurate prediction for properties difficult, especially for long-term corrosion. The purpose of this study is revealing the relationship between multiple environments and corrosion properties to predict the corrosion of cobalt-based alloys. Design/methodology/approach A data-driven method for the prediction of the corrosion behavior of cast and hot isostatic-pressed CoCrMo/W alloys in seawater is proposed. The gradient boosting regression models calculate mean relative errors (MREs) of 0.160 and 0.435 by evaluating a hold-out set for breakdown potential (Eb) and maximum current density (imax), respectively, considering various compositions, synthesis methods and corrosion environments. Findings The models can be used to estimate the “unseen” cobalt-based alloy after immersion in 3.5 Wt.% NaCl solution for one, two, four and eight months to obtain high precision with MREs of 7.8% and 9.8% for Eb and imax, respectively. Originality/value Machine learning method provides novel and promising insights for the prediction of localized corrosion properties.

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Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Cited by 151 publications

References 37 publications

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Improvement for Ti₃SiC₂/Cu joint brazed using composite fillers with abnormal expansion ceramic particulates

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning

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Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Cited by 151 publications

References 37 publications

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Improvement for Ti3SiC2/Cu joint brazed using composite fillers with abnormal expansion ceramic particulates

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning

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Improvement for Ti₃SiC₂/Cu joint brazed using composite fillers with abnormal expansion ceramic particulates