Articles
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Linking processing parameters with melt pool properties of multiple nickel-based superalloys via high-dimensional Gaussian process regression
J Mater Inf 2023;3:7. DOI: 10.20517/jmi.2022.38AbstractA physics-based model is used to predict the melt pool properties in the laser-directed energy ... MOREA physics-based model is used to predict the melt pool properties in the laser-directed energy deposition of several nickel-based superalloys for different process parameters. The input space is high-dimensional, consisting of a common 19-dimensional composition space for each alloy and the process parameters (laser power and scan velocity). Gaussian Process-based regression frameworks are developed by training surrogates on data generated by a validated analytical model. These surrogates are thereafter used to predict and define relationships between the composition, resultant thermophysical properties, process parameters, and the subsequent melt pool property. The probabilistic predictions are augmented by uncertainty quantification and sensitivity analysis to substantiate the findings further. LESS Full articleResearch Article|Published on: 31 Mar 2023 -
Development of an accurate “composition-process-properties” dataset for SLMed Al-Si-(Mg) alloys and its application in alloy design
J Mater Inf 2023;3:6. DOI: 10.20517/jmi.2023.03AbstractAl-Si-Mg series alloys are the most common alloys available for additive manufacturing forming with low ... MOREAl-Si-Mg series alloys are the most common alloys available for additive manufacturing forming with low cracking tendency. However, there is no systematic study on the computational design of SLMed Al-Si-(Mg) alloys due to the huge parameter space of composition and processes. In this paper, a high-quality dataset of SLMed Al-Si-(Mg) alloys containing 176 pieces of data from 50 publications was first established, which recorded the information, including alloy compositions, process parameters, test conditions, and mechanical properties. A threshold value of 35 J/mm3 for energy density (Ed) was then proposed as a criterion to clean the data points with lower ultimate tensile strength (UTS) and elongation (EL). The cleaned dataset consists of a first training/testing dataset with 142 data for model construction and a second testing dataset with 9 data for model verification. After that, four machine learning models were applied to establish the quantitative relation of “composition-processes-properties” in SLMed Al-Si-(Mg) alloys. The MLPReg model was chosen as the optimal one considering its best performance and subsequently utilized to design novel compositions and process parameters for SLMed Al-Si-(Mg) alloys. The UTS and EL of the designed alloy with a maximum comprehensive mechanical property are 549 MPa and 16%, both of which are higher than all the available experimental data. It is anticipated that the present design strategy based on the machine learning method should generally be applicable to other SLMed alloy systems. LESS Full articleResearch Article|Published on: 28 Mar 2023 -
Thermodynamic modeling of the Fe-Sn system including an experimental re-assessment of the liquid miscibility gap
J Mater Inf 2023;3:5. DOI: 10.20517/jmi.2022.37AbstractThe usage of low-grade ferrous scrap has increased over decades to decrease CO2 emissions and ... MOREThe usage of low-grade ferrous scrap has increased over decades to decrease CO2 emissions and to produce steel products at a low cost. A serious problem in melting post-consumer scrap material is the accumulation of tramp elements, e.g., Cu and Sn, in the liquid steel. These tramp elements are difficult to remove during conventional steelmaking processes. Sn is considered as one of the most harmful tramp elements because, together with Cu, it sometimes induces the liquid metal embrittlement in high-temperature ferrous processing, e.g., continuous casting and hot rolling. Furthermore, the chemical interaction between Fe and Sn plays an important role in the Sn smelting process. The raw material used in the Sn smelting process is SnO2 (cassiterite), in which Fe3O4 is a gangue in the Sn ore. In the process, the reduction of Fe3O4 is unavoidable, which results in forming a Fe-Sn alloy (hardhead). The recirculation of the hardhead decreases the furnace capacity and increases the energy consumption in the smelting. The need to efficiently recover Sn from secondary resources is therefore inevitable. The CALculation of PHAse Diagrams (CALPHAD) approach helps to predict the equilibrium state of the multicomponent system. Previously reported studies of the Fe-Sn system show inconsistencies in the calculations and the experimental results. Mainly the miscibility gap in the liquid phase was under debate, as experimental data of the phase boundary are scattered. Experimental study and re-optimization of model parameters were carried out with emphasis on the correct shape of the miscibility gap. Three different experimental techniques were employed: differential scanning calorimetry, electromagnetic levitation, and contact angle measurement. The present thermodynamic model has higher accuracy in predicting the solubility of Sn in the body-centered cubic (bcc), compared to previous assessments. This is achieved by re-evaluating the Gibbs energies of the FeSn and FeSn2 compounds and the peritectic reaction related to Fe5Sn3. Also, the inconsistencies related to the miscibility gap around XSn = 0.31-0.81 were resolved. The database developed in the present study can contribute to the development of a large CALPHAD database containing tramp elements. LESS Full articleResearch Article|Published on: 23 Mar 2023 -
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
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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 -
High-entropy alloy catalysts: high-throughput and machine learning-driven design
Review|Published on: 22 Nov 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 -
A review on high-throughput development of high-entropy alloys by combinatorial methods
Review|Published on: 17 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 -
Thermodynamic modeling of the Fe-Sn system including an experimental re-assessment of the liquid miscibility gap
Research Article|Published on: 23 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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