Ceramic electrochemical cells (CECs) are promising devices for clean and efficient energy
conversion and storage due to their high energy efficiency, more extended system durability …

Pseudocapacitance is capable of both high power and energy densities owing to its fast
chemical adsorption with substantial charge transfer. 2D transition‐metal carbides/nitrides …

Although deep-learning-based materials discovery has attracted considerable research
attention, the application of deep learning has been limited to discovery of materials within …

We developed an autonomous and efficient system for synthesising ferromagnetic materials
with large magnetocrystalline anisotropy by integrating theoretical, informatics, and …

Commenting about science has risks. Being critical sometimes raises strong opposing
reactions. People work so hard and leaders do not like to see their strategies under fire …

The complete automation of materials manufacturing with high productivity is a key problem
in some materials processing. In floating zone (FZ) crystal growth, which is a manufacturing …

A multimodal deep‐learning (MDL) framework is presented for predicting physical properties
of a ten‐dimensional acrylic polymer composite material by merging physical attributes and …

Twisted bilayer graphene (TBG) has drawn considerable attention due to its angle‐
dependent electrical, optical, and mechanical properties, yet preparing and identifying …

Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT)
forests, as growing certain desired properties can often come at the expense of other …

This chapter begins with an introduction, citing the relevant literature to explain the
background, practice, and future challenges of applying machine learning to the …