The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

Surrogate modeling has been popularized as an alternative to full-scale models in complex
engineering processes such as manufacturing and computer-assisted engineering. The …

The dominant paradigm of experiments in the social and behavioral sciences views an
experiment as a test of a theory, where the theory is assumed to generalize beyond the …

Abstract Machine learning (ML) is widely used to uncover structure–property relationships of
materials due to its ability to quickly find potential data patterns and make accurate …

Abstract Machine learning techniques were used to predict tensile properties of material
extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt …

Abstract Machine Learning has become more important for materials engineering in the last
decade. Globally, automated machine learning (AutoML) is growing in popularity with the …

Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

Material science has historically evolved in tandem with advancements in technologies for
characterization, synthesis, and computation. Another type of technology to add to this mix is …

Over the last few years there has been an increasing number of papers using machine
learning (ML) as a tool to aid research directed towards perovskite solar cells. This review …

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in
material characterization and synthesis requires strategies development for understanding …