Artificial Intelligence (AI) has advanced material research that were previously intractable,
for example, the machine learning (ML) has been able to predict some unprecedented …

Thermal camouflage technologies, which aim at blending the infrared (IR) signature of
targets into the background to counter the IR detection, have witnessed increasing …

Nanophotonic engineering provides an effective platform to manipulate thermal emission on-
demand, enabling unprecedented heat management superior to conventional bulk …

Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve
desired target emissivity spectra, as a typical emissivity engineering, for broad applications …

Conspectus In chemistry and materials science, researchers and engineers discover,
design, and optimize chemical compounds or materials with their professional knowledge …

Automated materials design with machine learning is increasingly common in recent years.
Theoretically, it is characterized as black-box optimization in the space of candidate …

Wavelength-selective thermal emitters (WS-EMs) are of interest due to the lack of cost-
effective, narrow-band sources in the mid-to long-wave infrared. WS-EMs can be realized …

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in
a large body of published work. However, only a small fraction of these publications includes …

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields
such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges …