Articles
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A review on high-throughput development of high-entropy alloys by combinatorial methods
J Mater Inf 2023;3:4. DOI: 10.20517/jmi.2022.41AbstractHigh-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands ... MOREHigh-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches. This review highlights the progress in combinatorial high-throughput studies towards rapid prediction, manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to HEAs and their unique properties. Then, this review describes high-throughput computational methods such as machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The next section presents advances in combinatorial synthesis of material libraries by additive manufacturing for efficient development of high-performance HEAs at bulk scale. The final section discusses the high-throughput characterization techniques used to accelerate the material property measurements for systematic understanding of the composition-processing-structure-property relationships in combinatorial HEA libraries. LESS Full articleReview|Published on: 17 Mar 2023 -
Sulfur poisoning mechanism of LSCF cathode material in the presence of SO2: a computational and experimental study
J Mater Inf 2023;3:3. DOI: 10.20517/jmi.2022.45AbstractAiming at the comprehensive understanding of the single sulfur poisoning effect and, eventually, the multiple ... MOREAiming at the comprehensive understanding of the single sulfur poisoning effect and, eventually, the multiple impurities poisoning phenomena on the SOFC (Solid Oxide Fuel Cell) cathode materials, the sulfur poisoning effect on the (La0.6Sr0.4)0.95Co0.2Fe0.8O3 (LSCF-6428) has been investigated in the presence of 10 ppm SO2 at 800, 900, and 1,000 °C, respectively, with a combined computational and experimental approach. The good agreement between the CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) simulations and the XRD (X-Ray Diffraction), SEM (Scanning Electron Microscopy), and TEM (Transmission Electron Microscopy) characterization results support the reliability of the CALPHAD approach in the SOFC field. Furthermore, comprehensive simulations were made to understand the impact of temperature, P(SO2), P(O2), and Sr concentration on the threshold of SrSO4 stability. Results showed that the formation of SrSO4 is thermodynamically favored at lower temperatures, higher P(SO2), higher P(O2), and higher Sr concentration. Finally, comparisons were also made between LSCF-6428 and LSM20 (La0.8Sr0.2MnO3) using simulations, which confirmed that LSCF-6428 is a poor sulfur-tolerant cathode, in agreement with the literature. LESS Full articleResearch Article|Published on: 9 Mar 2023 -
Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations
J Mater Inf 2023;3:2. DOI: 10.20517/jmi.2023.01AbstractThe escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of ... MOREThe escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data and allows for the deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data need to be managed, preserved and shared. The heterogeneity of such data calls for highly flexible models to represent the data from fabrication workflows, measurements and simulations. We propose the use of a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we create an ontology that serves as the blueprint of the data model. The Python framework Django is used to enable seamless integration into the virtual materials intelligence platform VIMI. The Django framework relies on the Object Graph Mapper neomodel to create a mapping between database classes and Python objects. The model can store the whole bandwidth of the data from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research. LESS Full articleResearch Article|Published on: 6 Mar 2023 -
Data-driven prediction of the glass-forming ability of modeled alloys by supervised machine learning
J Mater Inf 2023;3:1. DOI: 10.20517/jmi.2022.28AbstractThe ability of a matter to fall into a glassy state upon cooling differs greatly ... MOREThe ability of a matter to fall into a glassy state upon cooling differs greatly among metallic alloys. It is conventionally measured by the critical cooling rate Rc, below which crystallization inevitably happens. There are a lot of factors involved in determining Rc for an alloy, including both elemental features and alloy properties. However, the underlying physical mechanism is still far from being well understood. Therefore, the design of new metallic glasses is mainly by time- and labor-consuming trial-and-error experiments. This considerably slows down the development process of metallic glasses. Nowadays, large-scale computer simulations have been playing a significant role in understanding glass formation. Although the atomic-scale features can be well captured, the simulations themselves are constrained to a limited timescale. To overcome these issues, we propose to explore the glass-forming ability of the modeled alloys from computer simulations by supervised machine learning. We aim to gain insights into the key features determining Rc and found that the non-linear couplings of the geometrical and energetic factors are of great importance. An optimized machine learning model is then established to predict new glass formers with a timescale beyond the current simulation capability. This study will shed new light on both unveiling the glass formation mechanism and guiding new alloy design in practice. LESS Full articleResearch Article|Published on: 17 Feb 2023 -
Identifying stress-induced heterogeneity in Cu20Zr20Ni20Ti20Pd20 high-entropy metallic glass from machine learning atomic dynamics
J Mater Inf 2022;2:20. DOI: 10.20517/jmi.2022.29AbstractHigh-entropy metallic glasses (HEMGs) are amorphous alloys with a near-equiatomic composition containing at least five ... MOREHigh-entropy metallic glasses (HEMGs) are amorphous alloys with a near-equiatomic composition containing at least five elements. Such a unique non-crystalline structure with high configurational entropy of mixing provides HEMGs with promising prospects in applications, and it also attracts great scientific interest. In this paper, we focused on the atomic mechanism of stress-induced heterogeneity in the Cu20Zr20Ni20Ti20Pd20 HEMG. Applying the machine learning (ML) technique combined with the classical molecular dynamics (MD) simulation, we defined the liquid-like active atoms as the ones exhibiting high machine-learned temperature (TML). TML is a parameter to characterize the atomic motion activated by thermal and mechanical stimuli. The results reveal the stress-induced heterogeneity in atomic dynamics during creep. Local plastic flows originate from these active “hot” atoms, which have low five-fold symmetry, low coordination packing, and obvious chemical short-range ordering. Compared with conventional metallic glasses (MGs), the HEMG exhibits a smaller activation volume of creep, fewer active atoms, and sluggish dynamics. The results provide physical insights into the structural and dynamic heterogeneity in HEMGs at an atomic level. LESS Full articleResearch Article|Published on: 22 Dec 2022 -
High-entropy alloy catalysts: high-throughput and machine learning-driven design
J Mater Inf 2022;2:19. DOI: 10.20517/jmi.2022.23AbstractHigh-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising catalytic ... MOREHigh-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising catalytic performance. Most current studies focus on designing HEA catalysts through trial-and-error methods. This produces scattered data and is not conducive to obtaining a fundamental understanding of thestructure-property-performance relationships for HEA catalysts, thereby hindering their rational design.High-throughput (HT) techniques and machine learning (ML) methods show significant potential in generating, processing and analyzing databases with a vast amount of data, providing a new strategy for the further development of HEA catalysts. In this review, we summarize the recent literature on HT techniques for HEA synthesis, characterization and performance testing. We also review the ML models that are used to process and analyze existing databases to accelerate the discovery of HEA catalysts. Finally, the potential challenges and perspectives of HT techniques and ML models are presented to accelerate the discovery of new HEA catalysts and promote their development. LESS Full articleReview|Published on: 22 Nov 2022
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Generative deep learning as a tool for inverse design of high entropy refractory alloys
J Mater Inf 2021;1:3. DOI: 10.20517/jmi.2021.05AbstractGenerative deep learning is powering a wave of new innovations in materials design. This article ... MOREGenerative deep learning is powering a wave of new innovations in materials design. This article discusses the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships to generate novelty on demand, making them a valuable tool for materials informatics. LESS Full articlePerspective|Published on: 3 Sep 2021 -
Generative models for inverse design of inorganic solid materials
J Mater Inf 2021;1:4. DOI: 10.20517/jmi.2021.07AbstractOverwhelming evidence has been accumulating that materials informatics can provide a novel solution for materials ... MOREOverwhelming evidence has been accumulating that materials informatics can provide a novel solution for materials discovery. While the conventional approach to innovation relies mainly on experimentation, the generative models stemming from the field of machine learning can realize the long-held dream of inverse design, where properties are mapped to the chemical structures. In this review, we introduce the general aspects of inverse materials design and provide a brief overview of two generative models, variational autoencoder and generative adversarial network, which can be utilized to generate and optimize inorganic solid materials according to their properties. Reversible representation schemes for generative models are compared between molecular and crystalline structures, and challenges in regard to the latter are also discussed. Finally, we summarize the recent application of generative models in the exploration of chemical space with compositional and configurational degrees of freedom, and potential future directions are speculatively outlined. LESS Full articleReview|Published on: 13 Sep 2021 -
Development of robust surfaces for harsh service environments from the perspective of phase formation and transformation
J Mater Inf 2021;1:5. DOI: 10.20517/jmi.2021.02AbstractThe rise of the materials genome and materials informatics has enabled the accelerated development of ... MOREThe rise of the materials genome and materials informatics has enabled the accelerated development of robust surfaces for harsh service environments in the nuclear, aerospace and marine industries. Accurate information on the phase formation and transformation of materials (particularly coating materials) in synthesis and service processes is a prerequisite for the successful optimization of their properties. However, both these processes proceed under non-equilibrium conditions, making the traditional CALPHAD (CALculation of PHAse Diagrams) approach incapable of describing the phase relation and stability. Hence, this study provides a brief review on the recent research advances pertaining to the phase formation during coating deposition, the phase transformation in service and the materials optimization targeted for demanding working conditions. We also summarize the challenges of expanding phase diagram databases with a wide adaptability to metastable phase formation and non-equilibrium phase transformation in multicomponent systems. Through the elaboration of each research case, this review provides new insights into the surface protection of materials serving in harsh environments. LESS Full articleReview|Published on: 23 Sep 2021 -
Integrating computational materials science and materials informatics for the modeling of phase stability
J Mater Inf 2021;1:7. DOI: 10.20517/jmi.2021.06AbstractWith rapid developments in big data and artificial intelligence technologies, materials informatics has become a ... MOREWith rapid developments in big data and artificial intelligence technologies, materials informatics has become a new paradigm of materials science and engineering. In this review, the progress of modeling studies of phase stability in alloys is presented, with particular attention given to the development of the paradigm from traditional computational materials science (CMS) to materials informatics. The features of CMS models for phase stability studies are compared with those of data-driven approaches. The advantages of data-driven modeling in the framework of materials informatics are revealed. The approaches for developing interpretable machine learning, which has been mainly integrated with the developed CMS models and material science theories, are also discussed. Finally, the prospects for data-driven materials design based on the stability control of the dominant phases with regards to performance are proposed. LESS Full articleReview|Published on: 30 Sep 2021 -
Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques
J Mater Inf 2021;1:11. DOI: 10.20517/jmi.2021.10AbstractCasting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying ... MORECasting aluminum alloys are commonly used in industries due to their excellent comprehensive performance. Alloying/microalloying and post-solidification heat treatments are the most common measures to tune the microstructure for enhancing their mechanical properties. However, it is very challenging to achieve accurate and efficient development of novel casting aluminum alloys using the traditional trial-and-error method. With the rapid development of computer technology, the computational thermodynamics (CT) in the framework of the CALculation of PHAse Diagram approach, the data-driven machine learning (ML) technique, and also their combinations have been proved to be effective approaches for the design of casting aluminum alloys. In this review, the state-of-the-art computational alloy design approaches driven by CT and ML techniques, as well as their combinations, were comprehensively summarized. The current status of the thermodynamic database for aluminum alloys, as the core for CT, was also briefly introduced. After that, a variety of successful case studies on the design of different casting aluminum alloys driven by CT, ML, and their combinations were demonstrated, including common applications, CT-driven design of Sc-additional Al-Si-Mg series casting alloys, and design of Srmodified A356 alloys driven by combing CT and ML. Finally, the conclusions of this review were drawn, and perspectives for boosting the computational design approach driven by combining CT and ML techniques were pointed out. LESS Full articleReview|Published on: 30 Dec 2021
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Process parameter optimization of metal additive manufacturing: a review and outlook
Review|Published on: 9 Oct 2022 -
Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys
Research Article|Published on: 20 Sep 2022 -
Additive manufacturing as a tool for high-throughput experimentation
Viewpoints|Published on: 29 Aug 2022 -
Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments
Research Article|Published on: 24 Mar 2022 -
A metadata schema for lattice thermal conductivity from first-principles calculations
Research Article|Published on: 31 Oct 2022
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Data-driven prediction of the glass-forming ability of modeled alloys by supervised machine learning
Research Article|Published on: 17 Feb 2023 -
Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations
Research Article|Published on: 6 Mar 2023 -
Sulfur poisoning mechanism of LSCF cathode material in the presence of SO2: a computational and experimental study
Research Article|Published on: 9 Mar 2023 -
A review on high-throughput development of high-entropy alloys by combinatorial methods
Review|Published on: 17 Mar 2023
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About The Journal
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ISSN
2770-372X (Online)
Publisher
OAE Publishing Inc.
Article Processing Charges
$1200
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Editor-in-Chief
Tong-Yi Zhang
Publishing Model
Gold Open Access
Copyright
Copyright is retained by author(s)
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Quarterly
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Portico
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