A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.
Stanford researchers have created a system that enables efficient fabrication of complex three-dimensional (3D) nanostructures via triplet-triplet-annihilation upconversion (TTA-UC).
Researchers at Stanford have developed a new type of light source for spectroscopy applications, making it smaller and more energy efficient. Furthermore, this application allows a broad range of wavelengths without the interference from a pump laser.
The Fan Lab at Stanford University has developed an ultra-fast, physics-augmented, deep learning enhanced surrogate field solver for high-speed electromagnetic simulation and optimization. Denoising WaveY-Net uses a two-stage approach to target different field error sources.
Stanford Nanoscale and Quantum Photonics Lab researchers developed a passive, magnet free, integrated on-chip laser stabilization and isolation device. Lasers need a way to prevent the light they emit from reflecting into the laser and destabilizing it.
Active manipulation of light beams is required for a range of emerging optical technologies, including sensing, optical computing, virtual/augmented reality, dynamic holography, and computational imaging.
Researchers in Prof. Karl Deisseroth's laboratory have patented a revolutionary technique that can be utilized to map neural circuits in the whole brain.
Stanford inventors have developed a framework that performs digitally verifiable photonic matrix-vector multiplication in integrated photonic networks, which may potentially enable energy-efficient hash functions and cryptocurrency mining.