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Register a new userCombinatorial materials discovery strategy for high entropy alloy electrocatalysts using deposition source permutations
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High entropy alloys offer a huge search space for new electrocatalysts. Searching for a global property maximum in one quinary system could require, depending on compositional resolution, the synthesis of up to 10E6 samples which is impossible using conventional approaches. Co-sputtered materials libraries address this challenge by synthesis of controlled composition gradients of each element. However, even such a materials library covers less than 1% of the composition space of a quinary system. We present a new strategy using deposition source permutations optimized for highest improvement of the covered new compositions. Using this approach, the composition space can be sampled in different subsections allowing identification of the contribution of individual elements and their combinations on electrochemical activity. Unsupervised machine learning reveals that electrochemical activity is governed by the complex interplay of chemical and structural factors. Out of 2394 measured compositions, a new highly active composition for the oxygen reduction reaction around Ru17Rh5Pd19Ir29Pt30 was identified.
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Single-phase high-entropy monoborides (HEMBs) of the CrB prototype structure have been synthesized for the first time. Reactive spark plasma sintering of ball milled mixtures of elemental precursor powders produced bulk (V0.2Cr0.2Nb0.2Mo0.2Ta0.2)B, (V0.2Cr0.2Nb0.2Mo0.2W0.2)B, and (V0.2Cr0.2Nb0.2Ta0.2W0.2)B HEMB specimens of ~98.3-99.5% relative densities. Vickers hardness was measured to be ~22-26 GPa at an indentation load of 9.8 N and ~32-37 GPa at 0.98 N. In particular, the load-dependent hardness of (V0.2Cr0.2Nb0.2Ta0.2W0.2)B is higher than those of ternary (Ta0.5W0.5)B (already considered as superhard) and hardest reported high-entropy metal diborides, and on a par with the classical superhard boride WB4.
High-entropy alloys (HEAs) are solid solutions of multiple elements with equal atomic ratios which present an innovative pathway for de novo alloy engineering. While there exist extensive studies to ascertain the important structural aspects governing their mechanical behaviors, elucidating the underlying deformation mechanisms still remains a challenge. Using atomistic simulations, we probe the particle rearrangements in a yielding, model HEA system to understand the structural origin of its plasticity. We find the plastic deformation is initiated by irreversible topological fluctuations which tend to spatially localize in regions termed as soft spots which consist of particles actively participating in slow vibrational motions, an observation strikingly reminiscent of nonlinear glassy rheology. Due to the varying local elastic moduli resulting from the loss of compositional periodicity, these plastic responses exhibit significant spatial heterogeneity and are found to be inversely correlated with the distribution of local electronegativity. Further mechanical loading promotes the cooperativity among these local plastic events and triggers the formation of dislocation loops. As in strained crystalline solids, different dislocation loops can further merge together and propagate as the main carrier of large-scale plastic deformation. However, the energy barriers located at the spatial regions with higher local electronegativity severely hinders the motion of dislocations. By delineating the transient mechanical response in terms of atomic configuration, our computational findings shed new light on understanding the nature of plasticity of single-phase HEA.
High-entropy alloys (HEAs), which have been intensely studied due to their excellent mechanical properties, generally refer to alloys with multiple equimolar or nearly equimolar elements. According to this definition, Si-Ge-Sn alloys with equal or comparable concentrations of the three Group IV elements belong to the category of HEAs. As a result, the equimolar elements of Si-Ge-Sn alloys likely cause their atomic structures to exhibit the same core effects of metallic HEAs such as lattice distortion. Here we apply density functional theory (DFT) calculations to show that the SiGeSn HEA indeed exhibits a large local distortion effect. Unlike metallic HEAs, our Monte Carlo and DFT calculations show that the SiGeSn HEA exhibits no chemical short-range order due to the similar electronegativity of the constituent elements, thereby increasing the configurational entropy of the SiGeSn HEA. Hybrid density functional calculations show that the SiGeSn HEA remains semiconducting with a band gap of 0.38 eV, promising for economical and compatible mid-infrared optoelectronics applications. We then study the energetics of neutral single Si, Ge, and Sn vacancies and (expectedly) find wide distributions of vacancy formation energies, similar to those found in metallic HEAs. However, we also find anomalously small lower bounds (e.g., 0.04 eV for a Si vacancy) in the energy distributions, which arise from the bond reformation near the vacancy. Such small vacancy formation energies and their associated bond reformations retain the semiconducting behavior of the SiGeSn HEA, which may be a signature feature of a semiconducting HEA that differentiates from metallic HEAs.
High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org.
Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.
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