Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …

Cellular microstructures form naturally in many living organisms (eg, flowers and leaves) to
provide vital functions in synthesis, transport of nutrients, and regulation of growth. Although …

Materials with target nonlinear mechanical response can support the design of innovative
soft robots, wearable devices, footwear, and energy‐absorbing systems, yet it is challenging …

We report two generative deep-learning models that predict amino acid sequences and 3D
protein structures on the basis of secondary-structure design objectives via either the overall …

Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …

The simultaneous advances in artificial neural networks and photonic integration
technologies have spurred extensive research in optical computing and optical neural …

Phase-field modeling is an effective but computationally expensive method for capturing the
mesoscale morphological and microstructure evolution in materials. Hence, fast and …

Machine learning (ML) has emerged as an indispensable methodology to describe,
discover, and predict complex physical phenomena that efficiently help us learn underlying …

Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …