Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Develo** a deeper understanding of dynamic chemical, electronic, and morphological
changes at interfaces is key to solving practical issues in electrochemical energy storage …

Scientific advancement is universally based on the dynamic interplay between theoretical
insights, modeling, and experimental discoveries. However, this feedback loop is often slow …

The photon upconverting properties of lanthanide-doped nanoparticles drive their
applications in imaging, optoelectronics, and additive manufacturing. To maximize their …

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented
through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities …

Dielectric capacitors, characterized by ultra-high power densities, have been widely used in
Internet of Everything terminals and vigorously developed to improve their energy storage …

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in
material characterization and synthesis requires strategies development for understanding …

Microscopy has been pivotal in improving the understanding of structure‐function
relationships at the nanoscale and is by now ubiquitous in most characterization labs …

With the advent of large language models (LLMs), in both the open source and proprietary
domains, attention is turning to how to exploit such artificial intelligence (AI) systems in …

The unique aspect of hybrid perovskites is their tunability, allowing the engineering of the
bandgap via substitution. From the application viewpoint, this allows creation of tandem cells …