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

Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …

Peptide materials have a wide array of functions, from tissue engineering and surface
coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the …

Violating Kirchhoff's radiation law through magneto-optical materials or spatiotemporal
(Floquet) metamaterials can open a new door for engineering thermal radiation by breaking …

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …

Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …

In recent years,(big) data science has emerged as the “fourth paradigm” in physical science
research. Data-driven techniques, eg machine learning, are advantageous in dealing with …

Machine learning, applied to chemical and materials data, is transforming the field of
materials discovery and design, yet significant work is still required to fully take advantage of …

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is
azobenzene, which exhibits trans–cis isomerism in response to light. The thermal half-life of …

Light-matter interaction upon nanophotonic structures in the infrared wavelength has drew
increasing attentions due to the extensive potential applications. Among them …