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(a) Silicon primary phase. (b) Copper primary phase. (c) Gold primary phase. Composition of microstructures: ffi Au 1 Cu 1 Si 98 ; ffl Au 54 Cu 6 Si 40 ; Au 1 Cu 1 Si 98 ; Ð Au 45 Cu 39 Si 16 ; ð Au 81 Cu 9 Si 10 ; Þ Au 68 Cu 10 Si 22 .
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Composition libraries of Au-Cu-Si films comprising 800 composition patches were fabricated through co-sputtering deposition from elemental targets. The gold composition varies between 47% (compositions are in atomic percentage) and 81%, copper between 8% and 40%, and silicon between 6% and 36% within the library. We designed and used a high-through...
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... et al. J. Appl. Phys. 111, 114901 (2012) Subsequent microstructural investigations revealed the solidification product, and in particular the corresponding primary phase. On the Si-rich part of the library, the alloys form the diamond cubic silicon phase with a silicon compo- sition in excess of 98% (Fig. 6(a)). For the Au-rich alloys, fcc Au is the primary phase (Fig. 6(b)). A summary of the microstructural findings is shown in Figure 7. Primary phases formed during solidification can be divided into Au-, Cu-, and Si-rich ...
Context 2
... 111, 114901 (2012) Subsequent microstructural investigations revealed the solidification product, and in particular the corresponding primary phase. On the Si-rich part of the library, the alloys form the diamond cubic silicon phase with a silicon compo- sition in excess of 98% (Fig. 6(a)). For the Au-rich alloys, fcc Au is the primary phase (Fig. 6(b)). A summary of the microstructural findings is shown in Figure 7. Primary phases formed during solidification can be divided into Au-, Cu-, and Si-rich ...
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Zr-59 at.% Cu glassy film was deposited by co-magnetron sputtering using pure metal targets. The film exhibits an XRD-amorphous structure and smooth surface morphology. Isothermal annealing at 370 °C, for 30 and 60 min, modify the local structural ordering and promote nucleation of Cu10Zr7 nanocrystalline-like domains as confirmed by TEM analysis....
Citations
... One way is to directly cover the substrate with a physical metal mask of micron thickness during deposition to generate sample units 26,35 . An improved method is the use of a micro-machined Si deposition mask to mitigate deposition-shadowing effects by the creation of angled walls 36 . The sample units provide enough workspace for the subsequent characterization, which improves the accuracy of the experiment and reduces the difficulty of operation. ...
... The SEM results indicate that the column morphology changes are associated with the composition of the thin films 55 . In some cases, an optical microscope combined with a camera can take a series of images at short intervals, which can then be verified by SEM to measure structural evolution in situ 36 . Metallography reflects the microstructure information of alloy samples, to some extent, and can be used as an alternative method for high-throughput characterization. ...
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.
... To that end, higher throughput techniques for mapping structure-property relationships, in combination with combinatorial synthesis, have arisen as a promising approach for rapid and accurate determination of material properties through composition space. Indeed, successful applications of combinatorial approaches have been demonstrated across a wide range of systems, including multi-principal element alloys (MPEAs) [24], metallic glasses [25], and nanocrystalline alloys [18,23,26]. Recent work on amorphous complexion-containing ternary Al alloys with transition metal (TM) and rare earth (RE) dopants, Al-TM-RE, have exhibited exceptional high-temperature stability [22] and improved mechanical properties [23,27], with increasing dopant concentrations yielding consistently improved behaviors. ...
Segregation engineering has emerged as a promising pathway towards designing thermally stable nanocrystalline alloys with enhanced mechanical properties. However, the compositional and processing space for solute stabilized microstructures is vast, thus the application of high-throughput techniques to accelerate optimal material development is increasingly attractive. In this work, combinatorial synthesis is combined with high-throughput characterization techniques to explore microstructural transitions through annealing of a nanocrystalline ternary Al-base alloy containing a transition metal (TM=Ni) and rare earth dopant (RE=Y). A down-selected optimal composition with the highest thermal stability is annealed through in situ transmission electron microscopy, revealing that the removal of the RE species is correlated to a reduction in the microstructural stability at high temperatures as a result of variations in intermetallic phase formation. Results demonstrate the benefits of co-segregation for enhancing mechanical hardness and delaying the onset of microstructural instability.
... To that end, higher throughput techniques for mapping structure-property relationships, in combination with combinatorial synthesis, have arisen as a promising approach for rapid and accurate determination of material properties through composition space. Indeed, successful applications of combinatorial approaches have been demonstrated across a wide range of systems, including multi-principal element alloys (MPEAs) [24], metallic glasses [25], and nanocrystalline alloys [18,23,26]. Recent work on amorphous complexion-containing ternary Al alloys with transition metal (TM) and rare earth (RE) dopants, Al-TM-RE, have exhibited exceptional high-temperature stability [22] and improved mechanical properties [27,23], with increasing dopant concentrations yielding consistently improved behaviors. ...
Segregation engineering has emerged as a promising pathway towards designing thermally stable nanocrystalline alloys with enhanced mechanical properties. However, the compositional and processing space for solute stabilized microstructures is vast, thus the application of high-throughput techniques to accelerate optimal material development is increasingly attractive. In this work, combinatorial synthesis is combined with high-throughput characterization techniques to explore microstructural transitions through annealing of a nanocrystalline ternary Al-base alloy containing a transition metal (TM=Ni) and rare earth dopant (RE=Y). A down-selected optimal composition with the highest thermal stability is annealed through in situ transmission electron microscopy, revealing that the removal of the RE species is correlated to a reduction in the microstructural stability at high temperatures as a result of variations in intermetallic phase formation. Results demonstrate the benefits of co-segregation for enhancing mechanical hardness and delaying the onset of microstructural instability.
... With such a complex behavior, this system obviously calls for a high-throughput characterization, i.e., an approach that enables evaluating the properties over a wide range of chemical compositions, and, consequently, reaching a bigger-scale picture of how the alloying elements affect the targeted behavior. The approach of combinatorial sputtering, as a method most widely used for high-throughput characterization of alloys [35][36][37][38], cannot be utilized in this case, because of the unsuitability of the Ti-Nb system for fabrication of fully dense sputtered films [39] or sputtered films without composition-affected texture [40]. Moreover, bulk material is required for examination of mechanical properties, and also the heat treatment is required to follow the classical preparation route, which typically includes melting of the alloy, casting into appropriate shape, homogenization treatment, hot working [29,41] or cold working [24,26] procedure, and the final solution treatment which creates the low-modulus metastable β − condition. ...
Compositionally graded samples of Ti-XNb-7Zr-YO alloys (11≤X≤35, 0.49≤Y≤1.01) were prepared from elemental powders and TiO2 and compacted by the field-assisted sintering technique. By this approach, four layered samples covering together twenty four different chemical compositions were prepared. It allowed efficient sampling of alloys for potential biomedical use. Elastic constants of each layer (the shear modulus G and the Young’s modulus E) were determined using resonant ultrasound spectroscopy. The results show that in the considered compositional space, elastic constants exhibit a dependence on the volume fraction of β − stabilizing niobium and interstitial oxygen. Minimum elastic modulus was achieved for the compositions close to the stability limit between parent β matrix and martensitic α″ phase, while maximum elastic constants were caused by the presence of isothermal ω particles.
... To accelerate the compositional design for BMGs, the combinatorial methods have recently attracted a great deal of research attentions [18,19,[64][65][66][67][68][69][70]. These methods are usually based upon high throughput materials synthesis combined with high throughput structure and property characterization. ...
... Through the combinatorial design approach, one could prepare and characterize hundreds or even thousands of alloy compositions in a single run of experiment, which greatly improves the efficiency in compositional screening. To date, the most widely used high throughput method for materials synthesis is the MT-PVD [18,19,[64][65][66]. In MT-PVD, one can control the film composition gradient by tuning the angles and intensity ratio among energy fluxes of different targets. ...
... Following MT-PVD, rapid x-ray diffraction mapping could be applied to characterize the structure of the metallic thin films as a function of their chemical composition [18,19,66], which can quickly lead to the construction of a phase diagram. Aside from x-ray diffraction mapping, people devised other high throughput characterization techniques, such as density mapping [64], electric conductivity mapping [19], and solidification temperature mapping [65]. For example, Li et al systematically studied the GFA of the Cu-Zr films through density mapping [64]; Li et al searched the high GFA compositions in Ir-Ni-Ta films through electric resistance mapping [19]; and Ding et al [65] investigated the GFA of the Au-Cu-Si films [65] through an optical method that enabled the detection of melting and solidification of the metallic films, as seen in figure 2. Ding et al also studied BMG-forming compositional space through parallel blow forming, which characterized the thermoplastic formability of the Mg-Cu-Y films and casted quantitative insights into their GFAs [18]. ...
Metallic glasses or amorphous alloys are an important engineering material that has a history of research of about 80-90 years. While different fast cooling methods were developed for multi-component metallic glasses between 1960s and 1980s, 1990s witnessed a surge of research interest in the development of bulk metallic glasses. Since then, one central theme of research in the metallic-glass community has been compositional design that aims to search for metallic glasses with a better glass forming ability, a larger size and/or more interesting properties, which can hence meet the demands from more important applications. In this review article, we focus on the recent development of chemically complex metallic glasses, such as high entropy metallic glasses, with new tools that were not available or mature yet until recently, such as the state-of-the-art additive manufacturing technologies, high throughput materials design techniques and the methods for big data analyses (e.g. machine learning and artificial intelligence). We also discuss the recent use of metallic glasses in a variety of novel and important applications, from personal healthcare, electric energy transfer to nuclear energy that plays a pivotal role in the battle against global warming.
... Two main paradigms have emerged for tackling this problem. The first is to develop approaches that reduce the cost of experiments via combinatorial approaches [16][17][18][19], automation and synthesis planning. The second is to make use of surrogate models for high-throughput virtual screening. ...
As we enter the data age, ever-increasing amounts of human knowledge are being recorded in machine-readable formats. This has opened up new opportunities to leverage data to accelerate scientific discovery. This thesis focuses on how we can use historical and computational data to aid the discovery and development of new materials. We begin by looking at a traditional materials informatics task -- elucidating the structure-function relationships of high-temperature cuprate superconductors. One of the most significant challenges for materials informatics is the limited availability of relevant data. We propose a simple calibration-based approach to estimate the apical and in-plane copper-oxygen distances from more readily available lattice parameter data to address this challenge for cuprate superconductors. Our investigation uncovers a large, unexplored region of materials space that may yield cuprates with higher critical temperatures. We propose two experimental avenues that may enable this region to be accessed. Computational materials exploration is bottle-necked by our ability to provide input structures to feed our workflows. Whilst \textit{ab-intio} structure identification is possible, it is computationally burdensome and we lack design rules for deciding where to target searches in high-throughput setups. To address this, there is a need to develop tools that suggest promising candidates, enabling automated deployment and increased efficiency. Machine learning models are well suited to this task, however, current approaches typically use hand-engineered inputs. This means that their performance is circumscribed by the intuitions reflected in the chosen inputs. We propose a novel way to formulate the machine learning task as a set regression problem over the elements in a material. We show that our approach leads to higher sample efficiency than other well-established composition-based approaches. Having demonstrated the ability of machine learning to aid in the selection of promising compound compositions, we next explore how useful machine learning might be for identifying fabrication routes. Using a recently released data-mined data set of solid-state synthesis reactions, we design a two-stage model to predict the products of inorganic reactions. We critically explore the performance of this model, showing that whilst the predictions fall short of the accuracy required to be chemically discriminative, the model provides valuable insights into understanding inorganic reactions. Through careful investigation of the model's failure modes, we explore the challenges that remain in the construction of forward inorganic reaction prediction models and suggest some pathways to tackle the identified issues. One of the principal ways that material scientists understand and categorise materials is in terms of their symmetries. Crystal structure prototypes are assigned based on the presence of symmetrically equivalent sites known as Wyckoff positions. We show that a powerful coarse-grained representation of materials structures can be constructed from the Wyckoff positions by discarding information about their coordinates within crystal structures. One of the strengths of this representation is that it maintains the ability of structure-based methods to distinguish polymorphs whilst also allowing combinatorial enumeration akin to composition-based approaches. We construct an end-to-end differentiable model that takes our proposed Wyckoff representation as input. The performance of this approach is examined on a suite of materials discovery experiments showing that it leads to strong levels of enrichment in materials discovery tasks. The research presented in this thesis highlights the promise of applying data-driven workflows and machine learning in materials discovery and development. This thesis concludes by speculating about promising research directions for applying machine learning within materials discovery.
... Other approaches to measure T m have been demonstrated and may also prove to have utility. For example, an entire materials library was heated in a furnace, and phase transformations (including melting) were determined by changes in optical contrast as a function of the furnace temperature (58). This approach avoids the need for calibration and for making individual temperature measurements (they are all equal to the furnace temperature), but it may be experimentally challenging to apply at high temperatures. ...
Structural materials have lagged behind other classes in the use of combinatorial and high-throughput (CHT) methods for rapid screening and alloy development. The dual complexities of composition and microstructure are responsible for this, along with the need to produce bulk-like, defect-free materials libraries. This review evaluates recent progress in CHT evaluations for structural materials. High-throughput computations can augment or replace experiments and accelerate data analysis. New synthesis methods, including additive manufacturing, can rapidly produce composition gradients or arrays of discrete alloys-on-demand in bulk form, and new experimental methods have been validated for nearly all essential structural materials properties. The remaining gaps are CHT measurement of bulk tensile strength, ductility, and melting temperature and production of microstructural libraries. A search strategy designed for structural materials gains efficiency by performing two layers of evaluations before addressing microstructure, and this review closes with a future vision of the autonomous, closed-loop CHT exploration of structural materials.
Expected final online publication date for the Annual Review of Materials Science, Volume 51 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... To explore a large compositional range, the use of sample libraries with spatially resolved analysis techniques has been proven efficient (Sakurai et al., 2007.;Deng et al., 2007;Li et al., 2008Li et al., , 2019Aono et al., 2010Aono et al., , 2011Guo et al., 2011;Sakurai et al., 2011;Wang et al., 2011;Ding et al., 2012Ding et al., , 2014Gregoire et al., 2012;Flores, 2014, 2016;Liu et al., 2016; FIGURE 1 | Pair distribution function g(r) of experimentally and ab initio calculated Co 45.5 Fe 24 Ta 6 B 24.5 . Inset: structure factor S(q) of the experimental sample (Hostert et al., 2011). ...
... Schnabel et al., 2016b;Bordeenithikasem et al., 2017;Ren et al., 2018;Zhang et al., 2018;Zheng et al., 2019). Examples of synthesis routes for combinatorial samples are physical vapor deposition (Sakurai et al., 2007Deng et al., 2007;Li et al., 2008Li et al., , 2019Aono et al., 2011;Guo et al., 2011;Wang et al., 2011;Ding et al., 2012Ding et al., , 2014Gregoire et al., 2012;Liu et al., 2016;Schnabel et al., 2016b;Bordeenithikasem et al., 2017;Ren et al., 2018;Zhang et al., 2018;Zheng et al., 2019) or laser additive manufacturing Flores, 2014, 2016) of metallic glasses. ...
... High-throughput characterization techniques have been applied for metallic glasses to investigate glass forming ability, thermoplastic formability, composition, topology, and mechanical properties. However, most high-throughput studies are purely experimental, relying on trial and error (Sakurai et al., 2007Deng et al., 2007;Aono et al., 2010Aono et al., , 2011Guo et al., 2011;Wang et al., 2011;Ding et al., 2012Ding et al., , 2014Gregoire et al., 2012;Flores, 2014, 2016;Liu et al., 2016;Bordeenithikasem et al., 2017;Li et al., , 2019Zhang et al., 2018;Zheng et al., 2019); or empirical, such as machine learning approaches (Ren et al., 2018). In recent years, we combined ab initio calculations with high-throughput characterization techniques. ...
Quantum mechanically guided materials design has been used to predict the mechanical property trends in crystalline materials. Thereby, the identification of composition-structure-property relationships is enabled. However, quantum mechanics based design guidelines and material selection criteria for ultra-strong metallic glasses have been lacking. Hence, based on an ab initio model for metallic glasses in conjunction with an experimental high-throughput methodology geared toward revealing the relationship between chemistry, topology and mechanical properties, we propose principles for the design of tough as well as stiff metallic glasses. The main design notion is that a low fraction of hybridized bonds compared to the overall bonding in a metallic glass can be used as a criterion for the identification of damage-tolerant metallic glass systems. To enhance the stiffness of metallic glasses, the bond energy density must be increased as the bond energy density is the origin of stiffness in metallic glasses. The thermal expansion, which is an important glass-forming identifier, can be predicted based on the Debye-Grüneisen model.
... high-throughput analysis. Such approaches have been successfully applied to the study of metallic glasses [16][17][18][19] , high entropy alloys 20-22 , shape memory alloys 23,24 and microstructural evolution [25][26][27] . ...
Aluminum-based quasicrystals typically form across narrow composition ranges within binary to quaternary alloys, which makes their fabrication and characterization challenging. Here, we use combinatorial approaches together with fast characterization techniques to study a wide compositional range including known quasicrystal forming compositions. Specifically, we use magnetron co-sputtering to fabricate libraries of ~140 Al-Cu-Fe and ~300 Al-Cu-Fe-Cr alloys. The alloys compositions are measured through automated energy dispersive X-ray spectroscopy. Phase formation and thermal stability are investigated for different thermal processing conditions (as-sputtered and annealed at 400 °C, 520 °C and 600 °C for Al-Cu-Fe libraries; annealed at 600 °C for Al-Cu-Fe-Cr libraries) using automated X-ray diffraction and transmission electron microscopy. In both systems the compositional regions across which the quasicrystalline phase forms are identified. In particular, we demonstrate that the quasicrystalline phase forms across an unusually broad composition range in the Al-Cu-Fe-Cr system. Additionally, some of the considered alloys vitrify during sputtering, which also allows us to study their nucleation behavior. We find that phases with polytetrahedral symmetry, such as the icosahedral quasicrystal and the λ-Al13Fe4 phase, exhibit higher nucleation rates but lower growth rates, as compared to other phases with a lower degree of polytetrahedral order. Altogether, the here used combinatorial approach is powerful to identify compositional regions of quasicrystals.
... Also, Gremaud et al. [118,119] and Barcelo et al. [120] further applied the discrete deposition method to develop different kinds of hydrogen storage materials. In addition, Ding et al. [121,122] expanded co-sputtering approach to fabricate Au-Cu-Si and Mg-Cu-Y ternary metallic glass libraries on a silicon wafer as illustrated in Figure 2. Liu et al. [123,124] prepared multiple components thin-film metallic glasses library and explored composition dependence of microstructure and the interaction between antibacterial activity with compositions, respectively. Further- more, Etiemble et al. [125] investigated the application of Zrbased thin-film metallic glasses in biomedical field. ...
... With CXRD, the fastest measurement time could be decreased to 5 s. Using an optimized high-throughput XRD with Cu Kα radiation source from Rigaku SmartLab, Ding et al. [121,122,192] rapidly obtained the structure information of gold-based alloy libraries and metallic glasses thin-film libraries. The spatially resolution of this high-throughput approach had reached 2 mm. ...
Since the Material Genome Initiative (MGI) was proposed, high-throughput based technology has been widely employed in various fields of materials science. As a theoretical guide, material informatics has been introduced based on machine learning and data mining and high-throughput computation has been employed for large scale search, narrowing down the scope of the experiment trials. High-throughput materials experiments including synthesis, processing, and characterization technologies have become valuable research tools to pin down the prediction experimentally, enabling the discovery-to-deployment of advances materials more efficiently at a fraction of cost. This review aims to summarize the recent advances of high-throughput materials experiments and introduce briefly the development of materials design based on material genome concept. By selecting representative and classic works in the past years, various high-throughput preparation methods are introduced for different types of material gradient libraries, including metallic, inorganic materials, and polymers. Furthermore, high-throughput characterization approaches are comprehensively discussed, including both their advantages and limitations. Specifically, we focus on high-throughput mass spectrometry to analyze its current status and challenges in the application of catalysts screening.
