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

Growing materials data and data-driven informatics drastically promote the discovery and
design of materials. While there are significant advancements in data-driven models, the …

Abstract The field of Integrated Computational Materials Engineering (ICME) combines a
broad range of methods to study materials' responses over a spectrum of length scales. A …

A strategy for designing compositionally complex alloys (CCAs) achieving multiple
objectives is articulated. In this specific case, the objectives are low density and cost, along …

In metal additive manufacturing (AM), with sufficient understanding of process-structure–
property (PSP) relational linkages, the control of build parameters can produce parts with …

The foundation of materials science and engineering is the establishment of process–
microstructure–property links, which in turn form the basis for materials and process …

Construction of the structure-property (SP) relationship is an important tenet during materials
development. Optimizing microstructural information is a necessary and challenging task in …

For the engineering applications of freeze-cast porous ceramics, the demand targets are
often multiple and competing, which is a challenging problem to seek a Nash equilibrium in …

Finding efficient means of quantitatively describing material microstructure is a critical step
towards harnessing data-centric machine learning approaches to understanding and …

A knowledge and data-synergized intelligent computation architecture for materials was
proposed within the data science paradigm. As a vital operation, two digital ensemble …