In recent years, the development of artificial intelligence (AI) and machine learning (ML)
techniques has revolutionized composite design. Researchers have investigated intricate …

Structural characterization of polymer materials is a major step in the process of creating
complex materials design-structural-property relationships. With growing interests in artificial …

The exponential growth and success of machine learning (ML) has resulted in its application
in all scientific domains including material science. Advancement in experimental …

In materials research, structural characterization often requires multiple complementary
techniques to obtain a holistic morphological view of a synthesized material. Depending on …

In the pursuit of enhancing red phosphor materials, integrating Deep Learning (DL) and
machine Learning (ML) techniques has emerged as a transformative avenue. Challenges …

In the field of materials science, microscopy is the first and often only accessible method for
structural characterization. There is a growing interest in the development of machine …

In this paper, we present a new machine learning (ML) workflow with unsupervised learning
techniques to identify domains within atomic force microscopy (AFM) images obtained from …

Additive manufacturing (AM) has many advantages over conventional subtractive
manufacturing methods. The cost-effective AM allows for precise fabrication of complex …

Modern semiconductor processes are estimated to generate about 1 TB/tool/day of data,
including sensor data, event data, alarm data, and recipe information, amongst others. With …

Context In the last decade of data-driven decision-making, Machine Learning (ML) systems
reign supreme. Because of the different characteristics between ML and traditional Software …