The goal of this project is to build an autonomous AI application for supercomputers that can select and perform the simulation and machine learning tasks needed to identify better-performing molecules.

In this project, the team will carry out high-fidelity simulations of flow behavior and heat transfer mechanisms support the conversion of research nuclear reactors with involute-shaped fuel elements to low-enriched uranium.

This international project represents a convergence of a substantial fraction of the worldwide neutrino physics community, provided by the large investment by the Department of Energy (DOE). The primary scientific objectives of DUNE are to carry out a comprehensive investigation of neutrino oscillations to test charge and parity (CP) violation in the lepton sector, determine the ordering of the neutrino masses, and to test the three-neutrino paradigm (electron, muon and tau neutrino).

This project aims to resolve these issues by deploying in-house tools, solvers, and techniques that will readily leverage the capabilities of DOE supercomputing systems to obtain novel statistics and insights into the sub-Hinze-scale bubble population and accompanying gas dissolution.

This research is aimed at using large-scale, high-performance computing to assist discovery of novel battery electrolytes. The overall goal is to enable rational design of superior electrolytes for high voltage batteries. This study will focus on nontraditional electrolyte discovery from ionic liquids: a new entry to battery electrolytes.

In this project, the researchers will leverage these advances in order to compute the response functions in an important class of superconducting materials: the nickelates. In particular, they will compute the response functions in superconducting LaNiO2 across a range of temperatures and electron concentrations. These response functions and their structure in this parameter space will be analyzed in order to gain fundamental insight into unconventional superconductivity.

This research fulfills the vision of Department of Energy Office of Biological and Environmental Research (BER) Earth and Environmental Systems Science Division (EESSD) to “develop an improved capability for Earth system prediction on seasonal to multidecadal time scales” and addresses EESSD’s three Scientific Grand Challenges: the Integrated Water Cycle, Drivers and Responses in the Earth System, and DataModel Integration.

This project is tackling this challenge by using unique MCS observations from the Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) sites in the U.S. central Great Plains and the Amazon rain forest to evaluate the simulation of MCSs in state-of-the-art atmospheric models.

This project uses a multi-scale simulation approach to gain insights into the morphology of two distinct interfaces (polymer/inorganic and polymer/polymer interfaces) of solid polymer electrolytes, and their influence on overall ion conductivity. An important aspect of the project will be to develop new methods and augment software tools that will enable simulation of multiscale materials design problems, and which will be disseminate throughout the scientific and engineering communities.

The overall goal of this research is to accurately describe atomic nuclei (including their spectra, densities, structure functions, transitions, low-energy scattering, and responses) while simultaneously predicting the equation of state of neutron matter.

The objective of this research is to simulate the collapse of individual gas bubbles in channel flow, in order to understand the role of confinement and shear on the bubble dynamics and resulting shock waves and to connect these phenomena to cavitation damage.

The research goals of this project is to develop realistic predictive plasma models of disruptions that are integrated within a modern plasma control system. This novel integrated modeling tool would ideally lead to a mature AI-enabled comprehensive control system for ITER and future reactors that feature integration with full pilot-plant system models.

The research will address the DOE/BER mission by exploring underlying mechanisms of predictability and quantifying interactions of climate processes in how they affect US extremes and understand the current and future impacts of these phenomena on regional and global climate.

This research will provide support in creating the next-generation of nuclear power reactor design tools needed by industry and regulators to enable the deployment of advanced nuclear power. The data generated from these simulations will then be analyzed using both traditional and machine-learning methods to inform fast-running design tools that can be used directly by the industry.

Combining machine learning and atomistic simulations through active learning, this project will explore and rank a very large composition space of multicomponent oxides according to their stability and activity. The predictions of these models will then be validated in the lab and scaled up through collaborations with academics and industry.

This project will examine multimodal atomic imaging approaches enabled by the most intense femtosecond and attosecond XFEL pulses. The results from these simulations will provide predictions and new concepts to guide multimodal measurements using XFEL, and at the same time, maximize use of limited LCLS-II resources.

The goal of this project is to use a comparative framework that includes three state-of-the-art colorectal cancer models. Funded under the National Cancer Institute’s (NCI) Cancer Intervention and Surveillance Modeling Network (CISNET) program, these colorectal cancer models were independently developed for the evaluation of interventions, with emphasis on screening, and describe colorectal cancer natural history using different underlying assumptions.

This project will employ advanced ab initio quantum many-body techniques coupled with applied mathematics and computer science methods to study a wide range of nuclei and to accurately describe the atomic nucleus from first principles.

Researchers from the Southern California Earthquake Center (SCEC) are working to enhance their earthquake simulation and hazard mapping tools to provide the best possible information in terms of earthquake ground motion and seismic hazard.

This project consists of developing and implementing a novel in silico drug design method coupling ML and physics-based methods.