Machine learning and artificial intelligence can demonstrably accelerate scientific progress in predictive modeling for grand challenge areas such as the quest for clean energy via fusion power. This project seeks to expand modern convolutional and recurrent neural net software to carry out optimized hyperparameter tuning on exascale supercomputers to make strides toward validated prediction and associated mitigation of large-scale disruptions in burning plasmas such as ITER.

This project attacks the cancer “drug response problem” using a deep learning workflow that will search billions of virtual drug combinations aided by predictive models of cellular drug response.

This project uses advanced data science techniques to drive analysis of extreme-scale fluid-structure-interaction simulations, providing insights to better understand the role biological parameters play in determining tumor-cell trajectory in the circulatory system.

The ATLAS experiment at the Large Hadron Collider measures particles produced in proton-proton collision as if it were an extraordinarily rapid camera. These measurements led to the discovery of the Higgs boson, but hundreds of petabytes of data still remain unexamined, and the experiment’s computational needs will grow by an order of magnitude or more over the next decade. This project deploys necessary workflows and updates algorithms for exascale machines, preparing Aurora for effective use in the search for new physics.

Supercomputers have been guiding materials discovery for the creation of more efficient organic solar cells. By combining quantum-mechanical simulations with machine learning and data science, this project will harness exascale power to revolutionize the process of photovoltaic design and advance physical understanding of singlet fission, the phenomenon whereby one photogenerated singlet exciton is converted into two triplet excitons—increasing the electricity produced.

This project will develop data analytics and machine learning techniques to greatly enhance the value of flow simulations with the extraction of meaningful dynamics information. A hierarchy of turbulence models will be applied to a series of increasingly complex flows before culminating in the first flight-scale design optimization of active flow control on an aircraft’s vertical tail.

This project will connect some of the world’s largest and most detailed extreme-scale cosmological simulations with large-scale data obtained from the Legacy Survey of Space and Time (LSST) conducted at the Rubin Observatory. This astronomical survey will provide some of the most comprehensive observations of the visible sky to date. The simulations complement the observations by enabling unique probes of the universe using computations based on the underlying physics. By implementing cutting-edge data-intensive and machine learning techniques, this combined approach will usher in a new era of cosmological inference targeted at scientific breakthroughs.

This project will develop a computational pipeline for neuroscience that will extract brain-image-derived mappings of neurons and their connections from electron microscope datasets too large for today’s most powerful systems. Ultimately the pipeline will be used to analyze an entire cubic centimeter of electron microscopy data.

This project will determine possible interactions between nuclei and a large class of dark matter candidate particles. By coupling advanced machine learning and state-of-the-art physics simulations, it will provide critical input for experimental searches aiming to unravel the mysteries of dark matter while simultaneously giving insight into fundamental particle physics.

Chemical transformation technologies are present in virtually every sector, and their continued advancement requires a molecular-level understanding of underlying chemical processes. This project will facilitate and accelerate the quantitative description of crucial gas-phase and coupled heterogeneous catalyst/gas-phase chemical systems through the development of data-driven tools designed to revolutionize predictive catalysis and address DOE grand challenges.