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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. 

The design of structural and functional materials for specialized applications is experiencing significant growth fueled by rapid advancements in materials synthesis, characterization, and manufacturing, as well as by sophisticated computational materials modeling frameworks that span a wide spectrum of length and time scales in the mesoscale between atomistic and homogenized continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, there are several gaps in this framework as it relates to advanced structural materials development: (1) limited availability and access to high-fidelity experimental and computational datasets, (2) lack of co-design of experiments and simulation aimed at computational model validation, (3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, and (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation, and cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic and subsequent discussions. The proposal is to create a hub for "Mesoscale Experimentation and Simulation co-Operation (h-MESO)---that will (I) provide curation and sharing of models, data, and codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, and (III) provide a platform for education and workforce development. h-MESO will engage experimental and computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, and large-scale cyberinfrastructure initiatives. 

We develop an active workflow for calibrating microstructure–property relationships when a large dataset of microstructures is available, but the cost associated with evaluating the properties associated is high. 

Synthetic microstructure generation algorithms have emerged as a key tool for enabling large ICME and Materials Informatics efforts. In particular, statistically conditioned generative models allow researchers to systematically explore complex design spaces encountered in microstructure design. In spite of the engineering importance of polycrystalline materials, generative frameworks for these systems remain extremely limited. This stunted development – in comparison to the N-phase microstructure generation problem – occurs because of the complexities inherent to the representation of the polycrystalline orientation fields. For example, these fields exhibit multiple crystal- and sample-level symmetries. In prior work, these difficulties have resulted in instabilities in deep generative models for polycrystalline microstructures. In this work, we propose the use of a Reduced-Order Generalized Spherical Harmonic (ROGSH) basis to address the challenge described above. The proposed approach accounts for the complex sample- and crystal-level symmetries, and produces well behaved and low dimensional representations whose space has a meaningful Euclidean measure. We then demonstrate the ROGSH basis’s remarkable ability to produce stable denoising diffusion models by using our recently established Local–Global generative framework to create visually realistic synthetic polycrystalline microstructures. Furthermore, we demonstrate that the generation process can be conditioned on both first- and second-order spatial statistics of the polycrystalline orientation fields.