Nature Computational Science
A multidisciplinary journal that focuses on the development and use of computational techniques and mathematical models, as well as their application to address complex problems across a range of scientific disciplines.
Artificial intelligence enables precise integration of spatial transcriptomics data
With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets can provide more comprehensive characterizations of spatial tissue structures. We develop a powerful artificial intelligence method for integrating multiple spatial transcriptomics datasets.
An imaging-based approach to measure atmospheric turbulence
The strength information of atmospheric turbulence is hidden in its infrared imaging effects. We have developed a deep learning method to both extract the 2D atmospheric turbulence strength fields and clear and stable images from turbulence-distorted infrared images.
A crystal graph neural network model for defect formation in clean energy materials
Broad materials screening for defect properties requires fast methods that circumvent the need for first-principles supercell calculations of a vast number of possible defect configurations. We construct and train a defect graph neural network to screen oxides for clean energy applications.
