AI framework discovers microscopy designs 10,000x faster than traditional methods
The AI-driven optics simulator autonomously explores optical configurations to enhance super-resolution microscopy.
Researchers have developed an AI framework revolutionizing microscopy by autonomously designing experiments.
Named XLuminA, the platform optimizes processes 10,000 times faster than traditional methods, enhancing efficiency and innovation in scientific research.
According to the team at Max Planck Institute (MPI) in Germany, XLuminA is a free, open tool built with JAX, a fast computing library in Python. The framework’s modular design also enables easy adaptation to various microscopy and imaging techniques
“Experiments are our windows to the Universe, into the large and small scales. Given the sheer enormously large number of possible experimental configurations, its questionable whether human researchers have already discovered all exceptional setups. This is precisely where artificial intelligence can help”, said Mario Krenn, head of the “Artificial Scientist Lab” at MPI, in a statement.
AI revolutionizes microscopy
It takes years of effort for human researchers to find new methods for super-resolution microscopy. A microscope can have a vast array of optical settings, such as where to put mirrors or lenses.
The biological sciences currently employ optical microscopy the most. Super-resolution techniques break the traditional diffraction limit of light at roughly 250 nm and allow one to determine the organization of the smallest functional units of cellular life.
According to the team, as there are so many potential experimental optical configurations, developing novel microscopy techniques has historically depended on human experience, intuition, and imagination. For instance, there are now over 100 million possible combinations for an optical setup with just 10 pieces selected from 5 distinct components, like mirrors, lenses, or beam splitters.