High-throughput and data-driven machine  learning techniques for discovering high- entropy alloys.pdf
communications materials Review article https://doi.org/10.1038/s43246-024-00487-3 High-throughput and data-driven machine learning techniques for discovering high- entropy alloys Check for updates Lu Zhichao1,2, Ma Dong2, Liu Xiongjun1,3 & Zhaoping Lu 1,3 High-entropy alloys (HEAs) have attracted extensive attention in recent decades due to their unique chemical, physical, and mechanical properties. An in-depth understanding of the structure–property relationship in HEAs is the key to the discovery and design of new compositions with desirable properties. Related to this, materials genome strategy has been increasingly used for discovering new HEAs with better performance. This review paper provides an overview of key advances in this fast- growing area, along with current challenges and potential opportunities for HEAs. We also discuss related topics, such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. Finally, future research directions and perspectives for the materials genome-assisted design of HEAs are proposed and discussed. High-entropy alloys (HEAs), also called multi-principal element alloys1–3, are chemically disordered but topologically ordered with the formation of random solid-solution (SS) structures, such as face-centered cubic (FCC), body-centered cubic (BCC), or hexagonal-close-packed (HCP). Under- standingthecomposition–structure–propertiesrelationship haslongbeena topicofgreatinterestinHEAs.Thus,extensivestudieshavebeencarriedout on various HEAs, and many attractive properties have been achieved in the last two decades. These properties include good plasticity, high strength and hardness, outstanding high-temperature-softening resistance, and unique electrical and magnetic properties. In the past few years, besides metallic systems, high entropy materials have expanded to ceramics made of car- bides, borides, or nitrides of IV and V group transition metals, which have remarkable properties4–6. Due to these unique properties and large com- position space, high entropy materials have promising potential applica- tions under extreme conditions, such as, in high-temperature structural components, corrosion-resistant parts, coatings, and nuclear materials7. However, with regard to the property-oriented designs of HEAs, some challenges remain to be solved. (1) Owing to the chemically disordered structure, HEAs are not necessarily equimolar compositions; that is, many potentialelementsintheperiodictablecanconceivablybeincorporatedinto HEAs via microalloying or principal element substitution. Therefore, an essentially inﬁnite number of HEAs are available. Since the compositions of HEAs can be continuously adjustable, the properties of interest can be optimized. Conceptually, this poses a serious challenge—How can potential HEAs with properties of interest be ﬁne-tuned efﬁciently in such a large composition space rather than in a conventional “trial and error” manner8? (2) Coupled with the factthat fully understanding the complicated interplay between constituents and properties is a prerequisite when designing new HEAs, How can the intrinsic relationship in a vast and complex database be uncovered? To date, inspired by the Materials Genome Initiative (MGI), high-throughput techniques (preparation, characterization, and calcula- tion) and the data-driven machine learning (ML) method have been adopted by synergistically combining experiment, theory, and computation in a tightly integrated and high-throughput manner, and to predict and optimize HEAs at anunparalleledscale and in an effective way 9. These tools canbeusedtoscreenextensivecompositionspaceforadesiredpropertyand simultaneously pinpoint speciﬁc alloys with the desired properties. Speci- ﬁcally, high-throughput techniques are able to bridge the gap between experiments and ML modeling; that is, high-throughput approaches can provide valuable materials information for the following ML, and vice versa, ML can provide intelligent feedback to the experiments10–12. Through continuing efforts to integrate experiment, computation, and data-driven ML, the underlying structure–property relationships to the materials gen- ome can be revealed and thus seed a new generation of advanced HEAs13. This review aims to present a brief state-of-the-art overview of the materials genome strategy (MGS) applied in HEAs and provide a timely focus on key developments, including challenges and opportunities, in this interdisciplinary area. Speciﬁcally, we will give a brief introduction to the development of HEAs and the application of MGI in this ﬁeld. Additionally, some challenges will also be listed in a brief manner in “Introduction”. In section “High-throughput preparation and characterization of HEAs”, the main high-throughput preparation and characterization techniques for 1Beijing Advanced Innovation Center for Materials Genome Engineering, State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing, China. 2Songshan Lake Materials Laboratory, Dongguan, China. 3Institute for Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang, China. e-mail: xjliu@ustb.edu.cn; luzp@ustb.edu.cn Communications Materials |   (2024) 5:76  1 1234567890():,; 1234567890():,; HEAswillbe discussedindetailed and critical issuesneeded to be solved will also be proposed. In section “High-throughput computing for HEAs”, we will present and discuss applications of high-throughput computation method in accelerating the development of HEAs. An in-depth discussion about data-driven ML strategy for HEAs will be provided in section “Data- driven machine learning strategies “. Finally, in “Outlook” section, we will give an outlook of potential research activities to be exploited and main scientiﬁc challenges to be addressed in the future. The core purpose underlying the brief review is to provide an important opportunity to advance the understanding of MGS employed in HEAs and to offer researchers a platform to foster new ideas. High-throughput preparation and characterization of HEAs The design of HEAs poses a signiﬁcant challenge when exploring the phase structure and desirable properties through the vast potential multi- component compositional space available14. As such, unconventional high- throughput preparation techniques are crucially important, particularly for effectively narrowing down the alloys in a wide composition space. Among these, HEAs exploit a variety of preparation techniques, such as, combi- natorial thin ﬁlm deposition, laser additive manufacturing (LAM), rapid alloying prototype, diffusion multiples, and those based on welding. In what follows, we will give an overview of the different high-throughput techni- ques that were used to prepare multi-component HEAs and point out some critical issues that needed to be resolved. High-throughput preparation techniques for HEAs LAM. Combinatorial LAM endows the process with both high heating and cooling rates, and has been used as an efﬁcient method for the synthesis of HEAs. Among various LAM methods, laser metal deposition (LMD) is the preferred technique used to make HEA combinational libraries. During the LMD process, the feedstock nozzles convey the raw material powder to a rapidly moving melt pool formed by a laser through an inert gas ﬂow. Apparently, LMD is more suitable for high-throughput synthesis owing to the advantage of its real-time and variable feeding system, which applies two or more hoppers with different powder feeders to permit changes in the deposited powder compositions15–21. Combinatorial laser deposition of compositionally graded complex alloys has been regarded as an attractive approach for assessing the composition–microstructure–property relationships of HEAs. LMD is quite capable of synthesizing refractory HEAs that are difﬁcult to make19. Melia et al. prepared a MoNbTaW alloy system by additive manufacturing with commercial refractory elemental powders, which have good spherical morphology,leveragingtheadditivemanufacturingprocessandmechanical testing to enable rapid alloy exploration, as shown in Fig. 1. In the steady state, there was an evident linear spatial trend in the composition and a signiﬁcantlyvariation of hardness, withcomposition dominated bysolution strengthening (Fig. 1d)19. Compared to other mechanical properties (i.e., strength, plasticity, toughness, etc.), hardness is the simplest one that can be obtained effectively by mechanical testing automatically in areas with dif- ferent compositions of small samples. In view of the hardness–strength relationship (Hv ≈ 3σyðMPaÞ 9:81 )22, hardness allows for indirect and efﬁcient evaluations of mechanical properties. Borkar et al. studied the compositionally graded AlxCrCuFeNi2 (0 <x < 1.5) HEAs produced by laser deposition from a blend of elemental powders, using a double powder feeder with two hoppers containing CrCuFeNi2 and Al2CrCuFeNi2 powders, respectively. The sample of a cylindrical geometry was deposited with a smooth change of alloy com- position in height15. Additionally, an identical laser deposition processing method, laser-engineered net shaping (LENS), was also applied to construct the compositional and microstructural libraries of AlxCoCrFeNi in a high- throughput manner18. The discrepancy between LENS and the above- mentioned case was that the substrate (CoCrFeNi plate) for LENS was priorly made by an arc-melting and copper mold-casting method, while in Borkar’s work, a blend of powders of a nominal composition of CrCuFeNi2 was used. During the LENS process, the laser power and moving speed remained unchanged, and the feeding rateof Al powder for each monolayer patch increased in certain increments. The entire deposition process includes the addition of Al and two subsequent remelting processes per- pendicular to the deposition direction, to improve the mixing and com- positional homogeneity of the alloyed region18. In fact, the design and parameter adjustment of the LMD process has an important effect on sample preparation. For example, the substrate greatly inﬂuences the composition and microstructure of the deposited alloys, which can be improved by increasing the stack thickness or a rea- sonable experimental design. The former will not only increase the pre- parationcost,butwillalsoaffectthemicrostructureuniformity.Selectingthe maincomponentofthealloyasthesubstratematerial,depositingthesample in the thickness direction with less affection for the substrate, and a con- trolled composition gradient could form a reasonable experimental design17,23. Combinatorial deposition of thin ﬁlm materials libraries. Combina- torial thin ﬁlm synthesis by sputtering using multiple deposition sources is a state-of-the-art route for constructing of materials libraries that are composed of a wide range of gradually changed alloy compositions24,25. Continuous preparation of multiple gradients can be achieved by adjusting the processing parameters, such as the compositions of the targets, the power and angle of each gun, and the material and rotation of the substrate. For HEAs, several approaches based on sputtering have been employed for alloy design by tweaking these parameters25–30. Since HEAs contain more than three principal elements, the preparation of thin ﬁlm samples is an important part of high-throughput experiments. The composition library can be prepared by stacking multilayers, which were deposited by coordinating the single sputtering source with a removable mask. Due to the characteristics of the system alloys, the homogenization process requires a higher temperature with a long heat treatment duration. The substrate material and the experimental process are desired for higher requirements. The co-sputtering method, that is, using several targets to sputter simultaneously to obtain a composition library through the adjustment of processing parameters, is an ideal way to realize the compositional design of HEAs. However, the number of targetsavailable is often insufﬁcient tomeet the number of constituent elements. Considering the mechanical coordi- nationandcharacterizationaccuracy,thisissuecanbeeffectivelyresolvedby usingtargetsofmultipleelements.Atpresent,thesynthesisofcombinatorial ﬁlms is widely used in the preparation of a sample library of HEAs. Ludwig et al. reported quinary Ru–Rh–Pd–Ir–Pt composition spread thin-ﬁlm materials libraries that were co-sputtered from a load-lock-equipped combinatorial magnetron sputtering system. Fig. 2a shows the coverage of a ternary and a quaternary library, and Fig. 2b illustrates co-deposition from ﬁve deposition sources and compositional gradients of a co-sputtered quinary materials library. Six composition spread materials libraries were synthesized via target arrangement permutations, each of which comprises composition gradients in different subsections of the quinary composition space. Forty percent of all possible Ru–Rh-Pd–Ir–Pt HEA compositions (deﬁned by 10–35 at.% variations of individual elements) were covered by the materials libraries31. Additionally, Zhang et al. reported the Ti–Al–Cr–Fe–Ni composition library prepared via magnetron co- sputtering using Al, Ti, and CrFeNi targets29. They considered that the Cr, Co, and Fe atomic sizes are similar, and the inclusion of two magnetic components can avoid the interference of the magnetic ﬁeld. Elements with great differences in sputtering power cannot be placed on the same target simultaneously. Therefore, the components in the alloy are divided into Al, Ti,andCrFeNitargets,witha1:1:1compositionratiooftheCrFeNitarget.A similar method is also applied to the (Cr, Fe, V)–(Ta, W) alloy system28, which is regarded as a pseudo-binary alloy composed of transition group elements Cr, Fe,V, and refractory elements Ta, W. Samples fabricated using https://doi.org/10.1038/s43246-024-00487-3 Review article Communications Materials |   (2024) 5:76  2 66. Kim, H.-J. et al. High-throughput analysis of thin-ﬁlm stresses using arrays of micromachined cantilever beams. Rev. Sci. 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Acknowledgements This research was supported by National Natural Science Foundation of China (Nos. 52130108, 52301213, 52071024, and 52271003), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110805), National Key R&D Program of China (No. 2022YFA1603801), the Funds for Creative Research Groups of China (No. 51921001), the Open Fund of the https://doi.org/10.1038/s43246-024-00487-3 Review article Communications Materials |   (2024) 5:76  18 China Spallation Neutron Source Songshan Lake Science City (No. KFKT2023B11), Program for Changjiang Scholars and Innovative Research Team in University of China (No. IRT_14R05), and State Key Lab of Advanced Metals and Materials (No. 2022-ZD01). Author contributions Zhichao L. and Zhaoping L. conceived the idea and concept, wrote the initial manuscript, and validated the discussion. X.L. revised the paper. D.M. and Zhaoping L. supervised the work, led the project, and contributed to the ﬁnal writing. All authors discussed and approved the ﬁnal manuscript. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to Liu Xiongjun or Zhaoping Lu. Peer review information Communications Materials thanks Vineeth Venu- gopal, Ben Breitung, and the other, anonymous, reviewer(s) for their con- tribution to the peer review of this work. Primary Handling Editors: Eun Soo Park and John Plummer. Reprints and permissions information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral withregardtojurisdictional claims in published maps and institutional afﬁliations. 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Title: High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
Authors: Lu Zhichao, Ma Dong, Liu Xiongjun, Zhaoping Lu
Publisher:  Communications Materials, Springer Nature
Date: 2024-04-03 00:00:00
Abstract: High-entropy alloys (HEAs) have attracted extensive attention in recent decades due to their unique chemical, physical, and mechanical properties. ​ An in-depth understanding of the structure–property relationship in HEAs is the key to the discovery and design of new compositions with desirable properties. ​ Related to this, materials genome strategy has been increasingly used for discovering new HEAs with better performance. ​ This review paper provides an overview of key advances in this fast-growing area, along with current challenges and potential opportunities for HEAs. ​ We also discuss related topics, such as high-throughput preparation, characterization, and computation of HEAs, and data-driven machine learning for accelerating alloy development. ​ Finally, future research directions and perspectives for the materials genome-assisted design of HEAs are proposed and discussed. ​