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Abstract Resource management in engineering design seeks to optimally allocate while maximizing the performance metrics of the final design. Bayesian optimization (BO) is an efficient design framework that judiciously allocates resources through heuristic-based searches, aiming to identify the optimal design region with minimal experiments. Upon recommending a series of experiments or tasks, the framework anticipates their completion to augment its knowledge repository, subsequently guiding its decisions toward the most favorable next steps. However, when confronted with time constraints or other resource challenges, bottlenecks can hinder the traditional BO’s ability to assimilate knowledge and allocate resources with efficiency. In this work, we introduce an asynchronous learning framework designed to utilize idle periods between experiments. This model adeptly allocates resources, capitalizing on lower fidelity experiments to gather comprehensive insights about the target objective function. Such an approach ensures that the system progresses uninhibited by the outcomes of prior experiments, as it provisionally relies on anticipated results as stand-ins for actual outcomes. We initiate our exploration by addressing a basic problem, contrasting the efficacy of asynchronous learning against traditional synchronous multi-fidelity BO. We then employ this method to a practical challenge: optimizing a specific mechanical characteristic of a dual-phase steel. 

Interface free energy is a fundamental material parameter needed to predict the nucleation and growth of new phases. The high cost of experimentally determining this parameter makes it an ideal target for calculation through a physically informed simulation. Direct determination of interface free energy has many challenges, especially for solid–solid transformations. Indirect determination of the interface free energy from the nucleation data has been done in the case of solidification. However, a slow on molecular dynamics (MD) simulation time scale atomic diffusion makes this method not applicable to the case of nucleation from the solid phase when precipitate composition is different from that in matrix. To address this challenge, we outline the development of a new technique for determining the critical nucleus size from an MD simulation using a recently developed method to accelerate solid-state diffusion. The accuracy of our approach for the Ni–Al system for Ni3Al (γ′) precipitates in a Ni–Al (γ) matrix is demonstrated well within experimental accuracy and greatly improves upon previous computational methods [Herrnring et al., Acta Mater. 215(8), 117053 (2021)]. 

The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs) provides a basis for shape memory effect and superelasticity, thereby enabling applications requiring solid-state actuation and large recoverable shape changes upon mechanical load cycling. In order to tailor the transformation to a particular application, the compositional dependence of properties in NiTi-based SMAs, such as martensitic transformation temperatures and hysteresis, has been exploited. However, the compositional design space is large and complex, and experimental studies are expensive. In this work, we develop an interpretable piecewise linear regression model that predicts the parameter, a measure of compatibility between austenite and martensite phases, and an (indirect) factor that is well-correlated with martensitic transformation hysteresis, based on the chemical features derived from the alloy composition. The model is capable of predicting, for the first time, the type of martensitic transformation for a given alloy chemistry. The proposed model is validated by experimental data from the literature as well as in-house measurements. The results show that the model can effectively distinguish between B19 and regions for any given composition in NiTi-based SMAs and accurately estimate the lambda_2 parameter. 

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. The stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young’s, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior. 

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and the Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. We validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications.