Highlights

The dynamical mechanisms involved in light-matter interaction in materials, dominated by energetically excited quantum states, are notably complex. These complexities present a significant challenge for traditional scientific approaches. To address this, materials science groups at the Weizmann Institute are working on establishing a new theoretical learning algorithm. They are applying AI-based and machine learning approaches to evaluate and understand the outcomes of these intricate particle-particle interactions. This strategy will not only provide insights into the quantum behavior of materials when exposed to sunlight but also offers valuable information for optimizing material structures to enhance these interactions.

The functions that a network of neurons may perform is shaped by the map of connections between the neurons. Understanding the principles that govern the architecture of these networks of connections, known as "connectomes," is crucial for advancing our knowledge of brain development and architecture, how learning occurs (when these connections change), and the behavior these networks direct. Researchers at Elad Schneidman’s group are developing “generative models” for the connectomes of networks in the brains of different organisms: the olfactory bulb of zebrafish, the visual cortex of mice, and the complete neural network of the C. elegans worm.

Immune-checkpoint blockade therapies have revolutionized the treatment of metastatic melanoma in recent years. However, these therapies do not provide a long-lasting response for many patients. Indeed, 50% of patients succumb to metastatic disease within five years of diagnosis, with responses to immunotherapies even varying between different metastases within a single patient. The tumor microenvironment, specifically immune cells within it, plays a pivotal role in shaping tumor progression. Despite this, we still lack biomarkers to guide therapy and predict response to specific immunotherapies.