This INCITE Project takes a critical step toward understanding the behavior of black holes in the universe. 

The outcome of this INCITE project will contribute to solving the top global threats in the next decade—extreme weather and climate action failure.

With this INCITE project, researchers will advance the Automatic Building Energy Modeling (AutoBEM) project to extend existing capabilities for creating and simulating a model of every building in America (bit.ly/ModelAmerica) to estimate energy, emissions, and cost reductions of energy-efficient building technologies.

This INCITE project aims to reduce the computational and energetic costs of producing successful peptide macrocycle drugs or industrial enzymes using two approaches.

This INCITE project is aimed at uncovering flow physics, producing validation data for lower-fidelity simulation approaches, and supporting the development of improved predictive theory.

This project aims to predict and better understand the quantum-mechanical properties of materials that display novel properties, including novel quantum phases.

This INCITE project seeks to advance the current state of the art for online data analytics and machine learning (ML) applied to large-scale computational fluid dynamics simulations, as well as to develop more predictive lower-fidelity (and thus less computationally expensive) turbulence models for flows of interest to the aerospace, automotive, and renewable energy industries.

Building on previous INCITE research, this project employs 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.

With this INCITE project, these researchers will be able to conduct large-scale simulations of hypersonic flow fields based on the fundamental interactions of atoms and molecules in the gas.

The project carries out simulations of electronic excited state properties of heterogeneous materials by coupling first-principles molecular dynamics and electronic structure methods beyond density functional theory. 

Coupling machine learning and physics-based methods, with this work researchers aim to accelerate the slow process of drug discovery, which typically lasts many years and costs billions of dollars—a major weakness in public health emergencies.

With this INCITE project, researchers will study how magnetized plasma turbulence and magnetic reconnection — two of the most fundamental and ubiquitous plasma processes, which were historically studied separately, but have recently been shown to be inevitably interconnected — lead to heating and particle acceleration in the accretion flows feeding massive black holes.

This INCITE project uses use the gyrokinetic particle-in-cell code XGC to study the fundamental edge physics issues critical to the success of ITER and the magnetic fusion energy programs. 

With this new INCITE project, this team will conduct not only a full suite of 3D simulations for the spectrum of progenitor stars, but carry out these simulations for approximately five times the physical time possible with previous INCITE allocations all the way to the asymptotic state.

This INCITE project is using the full quantum mechanical predictive tools needed to quantitatively describe nuclear fission, collisions of heavy ions, and fusion—including the total kinetic energy released, the properties and excitation energies of the fission fragments, their masses, charges, excitation energies, angular momenta, the spectra of emitted neutrons, the multinucleon, and the energy transfer in low and medium energy heavy-ion collisions.

This project aims to create a new database of non-covalent interactions, in and out of equilibrium with a combination of density functional theory (DFT) with many-body-dispersion method (DFT+MBD) and the diffusion Monte Carlo (DMC) method.

The basis of this INCITE project is the realization that a catalytic interface in the steady state is in constant motion enabled by the reaction conditions (temperature and pressure of gases in thermal catalysis, or electrochemical potential, solvent and pH in electrocatalysis).

To overcome experimental limitations and improve image quality in materials characterization research, researchers are leveraging recent advancements in tomographic reconstruction algorithms, such as compressed sensing methods, to provide superior 3D resolution.

With this ADSP project, the team will use their recently developed self-supervised learning framework to extract meaningful representations from galaxy images in the Dark Energy Camera Legacy Survey dataset, providing a scalable data-driven approach capable of learning from unlabeled data.

This project makes a significant impact on the SBN and DUNE experiments through providing a common, scalable data reconstruction chain on a HPC system.