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 the definition of the "optimal processing region".

Materials 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.

Alloys 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-printed single-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.

High-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 the structure-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.

High-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 (T_ML). T_ML 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.