Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …

For more than two decades, there has been increasing interest in developing frameworks for
the accelerated discovery and design of novel materials that could enable promising and …

Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and
exploit materials design spaces. To date, most BO approaches to materials design have …

The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …

The design of materials and identification of optimal processing parameters constitute a
complex and challenging task, necessitating efficient utilization of available data. Bayesian …

Understanding the phase stability of a chemical system constitutes the foundation of
materials science. Knowledge of the equilibrium state of a system under arbitrary …

Understanding and controlling the thermal behavior during laser powder bed fusion (LPBF)
additive manufacturing (AM) is crucial to improve the quality of the printed layer and the …

Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …

The focus of goal-oriented materials design is to find the necessary chemistry/processing
conditions to achieve the desired properties. In this setting, a material's microstructure is …

Data-driven design shows the promise of accelerating materials discovery but is challenging
due to the prohibitive cost of searching the vast design space of chemistry, structure, and …