This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

The question of how methods from the field of artificial intelligence can help improve the
conventional frameworks for topology optimisation has received increasing attention over …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

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

Manipulating the architecture of materials to achieve optimal combinations of properties
(inverse design) has always been the dream of materials scientists and engineers. Lattices …

Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …

In the last few decades, the influence of machine learning has permeated many areas of
science and technology, including the field of materials science. This toolkit of data driven …

Kirigami-engineering has become an avenue for realizing multifunctional metamaterials that
tap into the instability landscape of planar surfaces embedded with cuts. Recently, it has …

Buckling-guided assembly of three-dimensional (3D) mesostructures from pre-defined 2D
precursor patterns has arisen increasing attention, owing to the compelling advantages in …

In this work, we propose a prediction model of the transverse mechanical behavior of
unidirectional (UD) composites containing complex microstructure with the help of a …