The design of chemical-based products and functional materials is vital to modern
technologies, yet remains expensive and slow. Artificial intelligence and machine learning …

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 …

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

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 …

This article presents an overview of the emerging themes in Autonomous Materials
Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing …

Microstructural properties of wormlike micelles (WLMs), which are employed in
characterizing the system to predict rheological properties, have long been obtained via …

In this work, we present a multifidelity Bayesian optimization framework for designing
architected materials with optimal energy absorption during compression. Data from both …

To know phase diagrams is a time saving approach for developing novel materials. To
efficiently construct phase diagrams, a machine learning technique was developed using …

Research on alloy phase diagrams started in the middle of the 19th century and progressed
into the laborious and time-consuming process of constructing phase diagrams through …