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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 As a first step in quantifying uncertainties in simulated Arctic climate response, Sandia researchers have performed a global sensitivity analysis (GSA) using a fully coupled ultralow-resolution configuration of the Energy Exascale Earth System Model (E3SM).  Coupled Earth system models are computationally expensive to run, making it difficult to generate the large ensembles required for uncertainty quantification.  In this research an ultralow version of E3SM was utilized to tractably investigate parametric uncertainty in the fully coupled model. More than one hundred perturbed simulation ensembles of one hundred years each were generated for the analysis and impacts on twelve Arctic quantities of interest were measured using the PyApprox library. The parameter variations show significant impact on the Arctic climate state with the largest impact coming from atmospheric parameters related to cloud parameterizations. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM. The results will be used to inform model tuning work as well as targeted studies at higher resolution.

Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).

Points of contact: Kara Peterson (kjpeter@sandia.gov)                Irina Tezaur (ikalash@sandia.gov)  

For more information on E3SM: https://e3sm.org/  

Ultra-low atmosphere grid (left) and ultra-low ocean grid (right).

Contact: Peterson, Kara J.
January 2021
1267024

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