Articles
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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 -
New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems
J Mater Inf 2022;2:18. DOI: 10.20517/jmi.2022.27AbstractAlloys with excellent properties are always in significant demand for meeting the severe conditions of ... MOREAlloys with excellent properties are always in significant demand for meeting the severe conditions of industrial applications. However, the design strategies of traditional alloys based on a single principal element have reached their limits in terms of property optimization. The concept of high-entropy alloys (HEAs) provides a new design strategy based on multicomponent elements, which may overcome the bottleneck problems that exist in traditional alloys. To further maximize the capability of HEAs, a novel additive manufacturing (AM) technique has been utilized to produce HEA components with the desired structures and properties. This review considers a new trend in the AM of HEAs, i.e., from the AM of single-phase HEAs to multiphase HEAs. Although most as-printedsingle-phase HEAs show superior tensile properties to as-cast ones, their strength is still not satisfactory, especially at elevated temperatures. Thus, multiphase HEAs are developed by introducing hard second phases, such as L12, BCC, carbides, oxides, nitrides, and so on. These phases can be introduced to the matrix using in situ alloying during AM or the subsequent heat treatment. Dislocation strengthening is considered as the main reason for improving the tensile properties of as-printed single-phase HEAs. In contrast, multiple strengthening and toughening mechanisms occur in as-printed multiphase HEAs, which can synergistically enhance their mechanical properties. Furthermore, machine learning provides an effective method to design new alloys with the desired properties and predict the optimal AM parameters for the designed alloys without tedious experiments. The synergistic combination of machine learning and AM will significantly speed up scientific advances and promote industrial applications. LESS Full articleReview|Published on: 17 Nov 2022 -
A metadata schema for lattice thermal conductivity from first-principles calculations
J Mater Inf 2022;2:17. DOI: 10.20517/jmi.2022.20AbstractMaterials genome engineering databases represent fundamental infrastructures for data-driven materials design, in which the data ... MOREMaterials genome engineering databases represent fundamental infrastructures for data-driven materials design, in which the data resources should satisfy the FAIR (Findable, Accessible, Interoperable and Reusable) principles. However, a variety of challenges, such as data standardization, veracity and longevity, still impede the progress of data-driven materials science, including both high-throughput experiments and simulations. In this work, we propose a metadata schema for lattice thermal conductivity from first-principles calculations. The calculation workflow for lattice thermal conductivity includes structural optimization and the calculation of interatomic force constants and lattice thermal conductivity. The data generated during the calculation process corresponds to the virtual sample information, virtual source data and processed data, respectively, as specified in the General rule for materials genome engineering data of the Chinese Society for Testing and Materials. Following this general rule, the metadata structure and schema for each action are systematically defined and all metadata elements can be collected completely. Although this metadata schema is specific to lattice thermal conductivity calculations, it provides general rules and insights for other computational materials data in materials genome engineering. LESS Full articleResearch Article|Published on: 31 Oct 2022 -
Process parameter optimization of metal additive manufacturing: a review and outlook
J Mater Inf 2022;2:16. DOI: 10.20517/jmi.2022.18AbstractThe selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it ... MOREThe selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it directly influences the defect formation and microstructure of the printed part. Over the past decade, research efforts have been devoted to identifying "optimal" processing regimes for different materials to achieve defect-free manufacturing, which mostly involve costly trial-and-error experiments and computationally expensive mechanistic simulations. Hence, it is apropos to critically review the methods used to achieve the optimal process parameters in AM. This work seeks to provide a structured analysis of current methodologies and discuss systematic approaches toward general optimization work in AM and the process parameter optimization of new AM alloys. A brief review of process-induced defects due to process parameter selection is given and the current methods for identifying "optimal processing windows" are summarized. Research works are analyzed under a standard optimization framework, including the design of experiments and characterization, modelling and optimization algorithms. The research gaps that preclude multi-objective optimization in AM are identified and future directions toward optimization work in AM are discussed. With growing capabilities in AM, we should reconsider the definition of the "optimal processing region". LESS Full articleReview|Published on: 9 Oct 2022 -
Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys
J Mater Inf 2022;2:15. DOI: 10.20517/jmi.2022.22AbstractAs promising next-generation candidates for applications in aero-engines, L12-strengthened cobalt (Co)-based superalloys have attracted extensive ... MOREAs promising next-generation candidates for applications in aero-engines, L12-strengthened cobalt (Co)-based superalloys have attracted extensive attention. However, the L12 strengthening phase in first-generation Co-Al-W-based superalloys is metastable, and both its solvus temperature and mechanical properties still need improvement. Therefore, it is necessary to discover new L12-strengthened Co-based superalloy systems with a stable L12 phase by exploring the effect of alloying elements on their stability. Traditional first-principles calculations are capable of providing the crystal structure and mechanical properties of the L12 phase doped by transition metals but suffer from low efficiency and relatively high computational costs. The present study combines machine learning (ML) with first-principles calculations to accelerate crystal structure and mechanical property predictions, with the latter providing both the training and validation datasets. Three ML models are established and trained to predict the occupancy of alloying elements in the supercell and the stability and mechanical properties of the L12 phase. The ML predictions are evaluated using first-principles calculations and the accompanying data are used to further refine the ML models. Our ML-accelerated first-principles calculation approach offers more efficient predictions of the crystal structure and mechanical properties for Co-V-Ta- and Co-Al-V-based systems than the traditional counterpart. This approach is applicable to expediting crystal structure and mechanical property calculations and thus the design and discovery of other advanced materials beyond Co-based superalloys. LESS Full articleResearch Article|Published on: 20 Sep 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 -
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 -
Machine learning-guided design and development of metallic structural materials
J Mater Inf 2021;1:9. DOI: 10.20517/jmi.2021.08AbstractIn recent years, the advent of machine learning (ML) in materials science has provided a ... MOREIn recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor. LESS Full articleReview|Published on: 24 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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