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Metal additive manufacturing (AM) holds immense potential for developing advanced structural alloys. However, the complex, heterogeneous nature of AM-produced materials presents significant challenges to traditional material characterization and optimization methods. This review explores the integration of artificial intelligence (AI) and machine learning (ML) with high-throughput material characterization protocols to rapidly establish the process–structure–property (PSP) relationships critically needed to dramatically accelerate the development of metal AM processes. Combinatorial high-throughput evaluations, including rapid material synthesis and nonstandard high-throughput testing protocols, such as spherical indentation and small punch tests, are discussed for their capability to rapidly assess mechanical properties and establish PSP linkages. Furthermore, the review examines the role of AI and ML in optimizing AM processes, particularly through Bayesian optimization, which offers new avenues for efficient exploration of high-dimensional design spaces. The review envisions a future where AI- and ML-driven, autonomous AM development cycles significantly enhance material and process optimization. 

The presence of higher vol% of gamma prime (γ′) in Nickel-based superalloys is crucial for achieving superior high-temperature strength and creep resistance properties. While directed energy deposition (DED) offers promising solutions for repairing these alloys, they usually lack the precipitation of γ′ phases due to rapid solidification. This study investigates the precipitation behavior in DED-produced Inconel 100 (IN100) superalloy during as-deposited and post-heat treatment conditions, focusing on the evolution of γ′ morphology, size, volume fraction, and their correlation with mechanical properties. Results obtained from the combination of experimental studies and CALPHAD-based thermodynamic simulations in as-deposited conditions showed the presence of a γ matrix with MC carbides (rich in Ti and Mo) and eutectic γ/γ' phases in the interdendritic region, which are deleterious to mechanical properties. A subsequent post-heat treatment dissolved these intermetallic phases and improved the vol% of γ′. The solution heat treatments form the γ' in complex structures, following the Ostwald ripening and reverse coarsening effects, where γ' was observed in spherical (< 0.1 μm), cubic (0.1–0.5 μm), and octet (> 0.5 μm) shapes. One-step age hardening significantly increased the volume fraction of γ′, changing the γ′ morphology to cubes. The presence of γ′ was further enhanced during a 2-step age hardening with the precipitation of secondary γ′. The γ′ precipitation behavior was statistically quantified using advanced digital image analysis protocols and analyzed using Gaussian Mixture Models (GMM). The findings offer valuable insights into tailoring microstructure and enhancing precipitation strengthening in AM IN100, with potential benefits for high-temperature aerospace applications.