Rising Stars in Computational & Data Sciences is an intensive academic and research career workshop series for women graduate students and postdocs. Co-organized by Sandia and UT-Austin’s Oden Institute for Computational Engineering & Sciences, Rising Stars brings together top women PhD students and postdocs for technical talks, panels, and networking events. The workshop series began in 2019 with a two-day event in Austin, TX. Due to travel limitations associated with the pandemic, the 2020 Rising Stars event went virtual with a compressed half-day format. Nonetheless, it was an overwhelming success with 28 attendees selected from a highly competitive pool of over 100 applicants. The workshop featured an inspiring keynote talk by Dr. Rachel Kuske, Chair of Mathematics at Georgia Institute of Technology, as well as lightning-round talks and breakout sessions. Several Sandia managers and staff also participated. The Rising Stars organizing committee includes Sandians Tammy Kolda (Distinguished Member of Technical Staff, Extreme-scale Data Science & Analytics Dept.) and James Stewart (Sr. Manager, Computational Sciences & Math Group), as well as UT Austin faculty Karen Willcox (Director, Oden Institute) and Rachel Ward (Assoc. Professor of Mathematics).

For more information on Rising Stars, see https://risingstars.oden.utexas.edu

Contact: Stewart, James R.
January 2021
2021-0274 O

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Researchers at Sandia and Los Alamos National Laboratories have discovered a new way to implement machine learning (ML) interatomic potentials in the LAMMPS molecular dynamics code. This makes it much easier to prototype and deploy ML models in LAMMPS and provides access to a vast reservoir of existing ML libraries.  The key is to define an interface (MLIAP) that separates the calculation of atomic fingerprints from the prediction of energy.  The interface also separates the atomic descriptors and energy models from the specialized LAMMPS data structures needed for efficient simulations on massively parallel computers. Most recently, a new model class has been added to MLIAP that provides access to any Python-based library, including the PyTorch neural network framework.  This advancement was made under the DOE SciDAC/FES FusMatML project for the application of machine learning to atomistic models for plasma-facing materials in fusion reactors.

More information on the LAMMPS package for machine-learned interatomic potentials can be found here: LAMMPS MLIAP Package

Contact: Thompson, Aidan P.
January 2021
1255798

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The IDAES Integrated Platform is a comprehensive set of open-source Process Systems Engineering (PSE) tools supporting the design, modeling, and optimization of advanced process and energy systems. By providing rigorous equation-oriented modeling capabilities, IDAES helps energy and process companies, technology developers, academic researchers, and the DOE to design, develop, scale-up, and analyze new PSE technologies and processes to accelerate advances and apply them to address the nation’s energy needs. The platform is based on and extends the Pyomo optimization modeling environment originally developed at Sandia.  IDAES has taken the core optimization capabilities in Pyomo and not only built a domain-specific process modeling environment, but also expanded the core environment into new areas, including logic-based modeling, custom decomposition procedures and optimization algorithms, model predictive control, and machine learning methods.

The IDAES PSE Computational Platform is developed by the Institute for the Design of Advanced Energy Systems (IDAES) and was recently awarded a 2020 R&D 100 Award.  Led by National Energy Technology Laboratory (NETL), IDAES is a collaboration with Sandia National Laboratories, Berkeley Lab, West Virginia University, Carnegie Mellon University, and the University of Notre Dame.

For more information on IDAES, see https://idaes.org

Contact: Siirola, John Daniel
January 2021
1255795

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In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes.  Analysts can now compare sets of surface meshes using synchronized 3D viewers, in which changing the viewpoint in one viewer changes viewpoints in all the others.  To illustrate this capability, the Slycat team performed an ensemble analysis for a material-modeling study that examines fracturing behavior in a plate after being impacted by a punch.  Input parameters include plate and punch density, friction coefficient, Young’s modulus, and initial punch velocity.  To compare different mesh variables over the same geometry, the analyst clones a mesh into multiple views, as shown in Figure 1.  The two runs represent opposite extremes for the initial punch velocity, with the 3D viewers in the top row showing the fastest initial velocity, and the viewers in the bottom row showing the slowest.  The mesh variables in the two rows are vertically matched top and bottom, so by comparing rows, you can compare the distinctly different stress behaviors of the extremes.

This new capability represents a significant advance in our ability to perform detailed comparative analysis of simulation results.  Analyzing mesh data rather than images provides greater flexibility for post-processing exploratory analysis.

Here we see 3 cloned viewers for each of 2 runs at timestep 400 (red and blue selected points). The clones are vertically matched between the 2 runs to display the same 3 variables: the cell-based variables of Von Mises and stress along the X-axis, and the first component of the point variable React. The top row is an example of a simulation using the fastest initial velocity value (blue scatterplot point), while the bottom row is an example of the slowest (red scatterplot point).

Contact: Crossno, Patricia J.
December 2020
2020-13393R

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In collaboration with researchers at Sandia, Kitware developers have made significant performance improvements to volume rendering for large-scale applications. First, Kitware significantly improved unstructured-grid volume rendering.  In a volume-rendering example for turbulent flow with 100 million cells on 320 ranks on a Sandia cluster, the volume rendered in 8 seconds using the new method, 122 seconds for the old method, making unstructured-grid visualization a viable in-situ option for applications.  Second, Kitware created a new "resample-to-image" filter that uses adaptive-mesh refinement to calculate and resample the image to the smaller mesh with minimal visualization artifacts.  The new filter reduces the amount of data required for visualization and provides a potential performance improvement (more testing is needed).  These improvements were driven by Sandia researchers for the NNSA Advanced Simulation and Computing program in support of the P&EM, V&V, and ATDM ASC sub-elements as part of a Large-Scale Calculation Initiative (LSCI) project. Kitware funding was provided through a contract with the ASC/CSSE sub-element.  Contacts for this work are Stefan Paul Domino (spdomin@sandia.gov), W. Alan Scott (wascott@sandia.gov), and Ron A. Oldfield (raoldfi@sandia.gov).

The image shows an unstructured volume-rendered Q-criterion field for a Reynolds # ~10,000 turbulent impinging jet. The performance improvements enabled rendering (for the first time) of the full unstructured dataset (nearly 2 billion Hexahedral elements). The rendering of this image was supported by the ASC LSCI portfolio.

Contact: Oldfield, Ron A.
November 2020
2020-12237 S

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