Chemical Space-Property Predictor Model of Perovskite Materials by High-Throughput Synthesis and Artificial Neural Networks

Chemical Space-Property Predictor Model of Perovskite Materials by High-Throughput Synthesis and Artificial Neural Networks

Academic Background Perovskite materials have attracted extensive attention due to their wide applications in solar cells and other electronic devices. Their optical properties (such as bandgap and lattice vibrations) can be flexibly modulated by tuning the chemical composition. Although the prediction of optical properties from perovskite structur...

De Novo Luciferases Enable Multiplexed Bioluminescence Imaging

De Novo Luciferases Enable Multiplexed Bioluminescence Imaging

Academic Background Bioluminescence technology is a highly sensitive and non-invasive imaging technique, enabling real-time monitoring in living organisms without the need for external light sources. Luciferase is the key enzyme that catalyzes the light-emitting reaction, but natural luciferases face multiple limitations, such as poor protein foldi...

Live Bacterial Chemistry in Biomedicine

Background Introduction The application of live bacteria in the biomedical field has garnered significant attention in recent years. Traditionally, bacteria were viewed as pathogens that needed to be eradicated. However, with the advancement of modern bacteriology, there has been a growing recognition of the complex symbiotic relationship between b...

Scaling of Hardware-Compatible Perturbative Training Algorithms

With the rapid development of artificial intelligence (AI) technology, artificial neural networks (ANNs) have achieved significant success in multiple fields. However, traditional neural network training methods—especially the backpropagation algorithm—face numerous challenges in hardware implementation. Although the backpropagation algorithm is ef...

Probing Nanoscale Structural Perturbation in a WS2 Monolayer via Explainable Artificial Intelligence

Background Introduction Two-dimensional (2D) materials exhibit significant potential in fields such as nanoelectronics and optoelectronics due to their unique physical and chemical properties. However, structural perturbations at the nanoscale have a profound impact on their performance. Traditional characterization methods like Raman spectroscopy,...

Learning Semantic Consistency for Audio-Visual Zero-Shot Learning

Academic Background In the field of artificial intelligence, Zero-Shot Learning (ZSL) is an extremely challenging task that aims to recognize unseen classes by leveraging knowledge from seen classes. Audio-Visual Zero-Shot Learning (AVZSL), a branch of ZSL, seeks to classify unseen classes by combining audio and visual information. However, many ex...