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The 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 what the definition of the "optimal processing region". LESS      Full article

As 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 article

Machine learning has promoted the rapid development of materials science. In this review, we provide an overview of recent advances in machine learning for inorganic phosphors. We take two aspects of material properties prediction and optimization based on iterative experiments as entry points to outline the applications of machine learning for inorganic phosphors in terms of Debye temperature prediction and luminescence intensity and thermal stability optimization. By analyzing the machine learning methods and their application objectives, current problems are summarized and suggestions for subsequent development are proposed. LESS      Full article

To achieve specific applications, it is always desirable to design new materials with peculiar topological properties. Herein, based on a D_2h B₂Cu₆H₆ molecule with the unique chemical bonding of planar pentacoordinate boron (ppB) as a building block, we constructed an infinite CuB monolayer by linking B₂Cu₆ subunits in an orthorhombic lattice. The planarity of the CuB sheet is attributed to the multicenter bonds and electron donation-back donation, as revealed by chemical bonding analysis. As a global minimum confirmed by the particle swarm optimization method, the CuB monolayer is expected to be highly stable, as indicated by its rather high cohesive energy, absence of soft phonon modes, and good resistance to high temperature, and thus is highly feasible for experimental realization. Remarkably, this CuB monolayer is metallic and predicted to be superconducting with an estimated critical temperature (T_c) of 4.6 K, and the critical temperature could be further enhanced by tensile strains (to 21 K at atmospheric pressure). LESS      Full article

Additive manufacturing (AM) is a disruptive technology with a unique capability in fabricating parts with complex geometry and fixing broken supply chains. However, many AM techniques are complicated with their processing features due to complex heating and cooling cycles with the melting of feedstock materials. Therefore, it is quite challenging to directly apply the materials design and processing optimization method used for conventional manufacturing to AM techniques. In this viewpoint paper, we discuss some of the ongoing efforts of high-throughput (HT) experimentation, which can be used for materials development and processing design. Particularly, we focus on the beam- and powder-based AM techniques since these methods have demonstrated success in HT experimentation. In addition, we propose new opportunities to apply AM techniques as the materials informatic tools contributing to materials genome. LESS      Full article

Thermodynamic databases are essential prerequisites for developing advanced materials, such as Ni-based superalloys. The present work collects a large amount of experimental and first-principles calculation data concerning the thermodynamics and phase diagrams of the Ni-Mo-Re system, based on which the thermodynamic properties of the ternary and its binary sub-systems Ni-Mo and Mo-Re are assessed by means of the CALculation of PHAse Diagrams (CALPHAD) approach. The thermodynamic database containing all model parameters is established and most experimental data are reproduced satisfactorily. The present work demonstrates the use of the CALPHAD method as a practical appliance in the toolbox of materials informatics to analyze and discriminate various types of data by thermodynamic modeling and then produce more useful data in wider ranges of compositions and temperatures by computational predictions. LESS      Full article