Los Alamos National Labs with logo 2021

Computational Physics Student Summer Workshop

Sponsored by the Los Alamos National Laboratory Advanced Scientific Computing (ASC) Program

Workshop Leads  

Workshop participants working

Lectures, Teamwork, and Mentoring: Integrated to help you learn, enhance your career, and build connections for the future

2021 Computational Physics Summer Workshop

June 7 - August 14, 2021

The Summer 2021 workshop application process is now complete. Applicants will soon be notified of their selection status.

Los Alamos National Laboratory's X Computational Physics Division, in cooperation with other related divisions including Theoretical Design and Computer, Computational, and Statistical Sciences is pleased to sponsor the 2021 Computational Physics Student Summer Workshop. The workshop seeks to bring to the Laboratory a diverse group of exceptional undergraduate and graduate students from within the United States for informative, enriching lectures and to work with its staff for 10 weeks on interesting and relevant projects that may culminate in articles or conference presentations.

Workshop Overview

Students are organized into teams of 2 working under the guidance of one or more mentors. Each participant is awarded a fellowship that typically ranges from $8,000 to $13,000 based on academic rank (junior, senior, 1st year graduate student, etc.). Lectures, Teamwork, and Mentoring are integrated to help you learn about computational physics and enhance your career. Shared technical goals help you build connections for the future. Generous Fellowships are awarded to support your educational and research efforts while in the summer workshop. Social Events and Tours enhance the team-building experience, creating lasting memories and professional relationships.

Note: Due to the current health pandemic, the Summer 2021 internship will be held remotely. Student projects will still be offered along with a series of career-enrichment talks and virtual social hours

Application Guidelines

The workshop is open to U.S. Citizens who have completed at least one year of college or university. Applications must be submitted online. As part of the application process, you will need to provide a cover letter and resume (your school may have a career office that can help with these), and transcripts (unofficial transcripts are fine), as well as the name and contact information for one person who will submit a letter of recommendation on your behalf.  If you are accepted, you will be required to submit official transcripts. An additional part of the application is to select your top three choices for your research project this summer. A description of the topics are listed below.

The Summer 2021 workshop application review process is now complete. Applicants will soon be notified of their selection status.

Fellowship Stipend

Participants will receive a fellowship stipend, the amount to be determined based on your current academic rank.  The stipend will be paid in three installments over the course of the summer.  You will be responsible to cover your own travel, food, and housing.  Housing is in short supply in Los Alamos during the summer, but we will do our best to provide resources to find housing.


For the 2021 workshop, there will be 12 different projects for students to choose from. Students will work in pairs and will be assigned various mentors that will oversee their project. The mentors are all established scientists at Los Alamos National Laboratory. For more detailed project information, please click on the topic names below.

High performance computing for parametric trends in nuclear reactions

Mentors: Amy Lovell and Matthew Mumpower

Description:Trends are observed across isotopic chains (groups of nuclei having the same charge but different numbers of neutrons), which are reflected in the models used to nuclear properties.  However, for heavier mass nuclei, such as actinides, direct model calculations are either too computationally expensive or impossible to perform, so phenomenological models are fitted across a wide region of the nuclear chart.  To perform reaction calculations for specific nuclei these global models are further optimized to the system of interest, with no account taken for previously optimized parameters of neighboring nuclei which should be similar.  This project will give students an opportunity to learn about Bayesian inference as well as model emulation (for CPU and GPU) by performing optimization of model parameters for several neighboring nuclei along an isotope chain.  Such a procedure allows us to understand how the mean parameter values, uncertainties, and correlations change along the isotopic chain and how these values change when different optimization schemes and model emulators are used.  Trends in the parameter values can then be determined and used to make predictions for reactions not included in the optimization. 

A molecular dynamics study of shear viscosity in liquid-like plasma mixtures

Mentors: Brett Scheiner and Josh Sauppe

Description: In addition to their interaction via long range electrostatic forces, plasmas can exhibit gas, liquid, and solid-like behavior depending on their density and temperature. In some laser driven inertial confinement fusion targets, the initial shock compressed plasma can temporarily enter the liquid-like regime. In these experiments, non-uniformity in the target and laser radiation can seed hydrodynamic instability growth (e.g. Rayleigh Taylor or Richtmyer-Meshkov Instability). However, fluid properties such as diffusion and viscosity can reduce the rate of this growth. The goal of this project is to determine if the viscosity can be increased or modified through the addition of highly charged dopants which interact strongly with the other electrons and ions due to their charge. This study will utilize large-scale molecular dynamics simulations to study plasma properties on a first-principles basis.

Deep learning for satellite imagery: modeling atmospheric effects

Mentors: Bertrand Rouet-Leduc and Christopher Ren

Description: Rapid and large amplitude ground deformation such as induced by large magnitude earthquakes are now routinely imaged by satellite-based Interferometric Synthetic Aperture Radar (InSAR).  However, measuring smaller amplitude signals remains challenging due to atmospheric propagation delays which may exceed the signature of deformation in InSAR time series. Although atmospheric correction methods improve our ability to observe slow and small (i.e. mm/yr) deformations, expert interpretation and a priori knowledge of deforming systems is always required to highlight deformation signals. In our initial work we developed a deep learning architecture tailored to remove atmospheric delays due to turbulence and layering of the atmosphere, as well as to identify and extract transient episodes of ground deformation. In this project you will be exploring how to better simulate noise sources in InSAR data (atmospheric, ionospheric, but also seasonal ground deformation), as well as exploring deep learning architectures and methods that can push InSAR data analysis towards automation at a global scale.

Modeling and simulation of nuclear weapons effects

Mentors: Nathan Woods and Jeremy Best

Description: The study and understanding of the effects of nuclear weapons effects is an important part of our national security. Whether planning our own strikes, designing hardened facilities, or preparing to coordinate emergency response to a hostile attack, we must be able to predict damage that may result from a variety of different scenarios. We will use high-fidelity multiphysics simulation codes to model the effects of nuclear explosions, and we will compare the results of these calculations with publicly available data from the United States’ nuclear test history.

Uncertainty Quantification in High Explosive Equations of State

Mentors: Jeffery Leiding, Stephen Andrews, Chris Ticknor

Description: Understanding how matter behaves in extreme conditions is of importance to many areas of study, such as earth science, astrophysics, and weapons physics. The behavior of energetic materials is particularly intriguing. LANL has developed a thermochemical code called MAGPIE, which uses statistical mechanics to describe the behavior of molecular mixtures in extreme conditions. We are interested in studying how uncertainties in the input to MAGPIE (such as molecular properties) affect the resultant behavior of high explosives. Opportunities exist in this project to learn about high explosives, molecular physics, Bayesian techniques, uncertainty quantification, and hydrodynamic simulation of high explosives.

Uncertainty Quantification in ab-initio equations of state

Mentors: Daniel Rehn (XCP-5),  Ann Mattsson (XCP-5),  Daniel Sheppard (XCP-5), Carl Greeff (T-1)

Description: Materials equations of state (EOS) are important for a wide variety of engineering applications, including their use in hydrodynamics codes to inform on materials properties in different environments.  In this project, we will construct EOS's using first-principles (ab-initio) calculations.  In particular, we will use density functional theory (DFT) to calculate materials phase diagrams using different approximations for the exchange-correlation functional, as well as different approximations for computing the vibrational properties of materials, such as phonon calculations and molecular dynamics calculations.  By assessing the error and uncertainty in these different approximations, we will quantify the overall uncertainty in the resulting EOS.  Students will learn the fundamentals of DFT and will gain experience using cutting-edge DFT codes.

Nuclear Data Verification Testing and Perturbation

Mentors: Nathan Gibson and Wim Haeck

Description: Scientists at LANL have recently developed a robust tool known as ENDFtk to interact with nuclear data in a modern way, both via C++ and Python APIs.  This work will develop a nuclear data validation testing framework, where these computational tools can be used to perform physics checks of new and legacy evaluated nuclear data files, and a tool for perturbing nuclear data, which is necessary for sensitivity and uncertainty quantification analyses.  Students will use and develop modern C++ and Python code, learn about and work with evaluated nuclear data, and participate in high-visibility laboratory efforts.  Applications sustained by nuclear data at the lab are wide-ranging, including national security, energy, astrophysics, and medicine.

History Dependent Interatomic Potentials for Non-Equilibrium Materials.

Mentors: Benjamin Nebegen and Justin Smith

Description: Molecular dynamics, the propagation of atoms through the use of Newton’s equations, provides a powerful  tool to understand the fundamental physics behind macroscopic properties of metals and materials.  Computation of forces needed to integrate Newton’s equations can either be done with accurate though expensive quantum mechanical methods, or through approximate but fast classical force fields. Recently developed Machine Learned interatomic potentials, which can be directly trained to data obtained from quantum mechanical methods, provide a pathway for highly accurate and scalable molecular dynamics simulations. These potentials, however, have no knowledge of the electronic state of a given material, limiting their usefulness in cases where electronic temperature, ionization  state, or other electronic properties change throughout a simulation. In this project we will develop a framework utilizing Recurrent Neural Network architectures to track electronic state information throughout a molecular dynamics simulation for more accurate treatment of variable electronic state effects.

Numerical study of particle jetting from the explosive dispersal of particles

Mentors: Johnathan Regele and Yash Mehta

Description: The objective of this project is to understand how particle jetting occurs in the explosive dispersal of particles. Experiments show that during the later stages after an explosion, particles tend to cluster into jets of solid material. In this project, an advanced research code will be used to simulate a shock wave as it passes through a cloud of 2D fully resolved cylinders that behave like particles. We will study a range of particle Reynolds numbers and volume fractions to understand how the jetting phenomena occurs for a variety of conditions.

Charged Particle Transport Verification and Validation

Mentors: Edward Norris and Benjamin Ryan

Description: Modeling charged particle transport (CPT) is important for developing predictive plasma physics applications. A Monte Carlo implementation of CPT was recently added to the LANL multi-physics code Lumos. In this project, students will perform a verification and validation study of the CPT capability in Lumos. The study will consist of a combination of comparison to analytical models, the Jayenne CPT library, and previously published experimental data.

How stable are molecules in white dwarf atmospheres?

Mentors: Didier Saumon and Simon Blouin

Description: After exhausting their fuel, most stars end their lives as white dwarfs, dense stellar embers that cool down for the rest of time. Their simple evolution means that their ages can be easily determined from their observable properties, making those dead stars unique cosmic clocks that can be applied to galactic archeology. A key uncertainty of current models (on which age determinations depend) concerns the dissociation equilibrium of diatomic molecules (chiefly H_2 <--> 2H and C_2<--> 2C) in a bath of dense helium atoms under fluid-like conditions. This project applies concepts of quantum mechanics, statistical physics and thermodynamics, combined with computer simulations to compute the chemical equilibrium of interacting molecules and atoms. Students will use density functional theory (DFT) and molecular dynamics simulation techniques to address this question and improve the reliability of white dwarf models.

Multigroup Scattering in Monte Carlo Radiation Transport Codes.

Mentors: Tim Burke and Travis Trahan

Description: Monte Carlo codes at LANL rely on continuous-energy scattering mechanics to sample directions and angles of particles emitted from collisions whereas deterministic radiation transport codes are forced to discretize in energy and angle: defining probabilities of scattering from one energy group to another and defining the angular distribution emitted from such a collision via an expansion of Legendre polynomials. This discretization introduces approximations to the solution of the transport simulation but it also results in a simplified description of scattering that may be more performant than continuous-energy scattering mechanics, especially on GPUs. The difficulty with implementing multigroup scattering in Monte Carlo codes lies in the negative values that result from a finite polynomial expansion of the scattering distributions. However, it is possible to produce distributions that preserve the moments of the multigroup scattering distributions that are purely positive and thus amenable to sampling in Monte Carlo codes. Students will implement the methods from literature in a GPU-enabled multigroup Monte Carlo code, verify the methods, and conduct performance analysis of the methods on GPUs and CPUs.