National Renewable Energy Laboratory
"Developing a State-by-State Understanding of Polarizability with the GW Approximation"
Olivia Hull, Kansas State University
Practicum Year: 2019
Practicum Supervisor: Derek Vigil-Fowler, Research Scientist: High Performance Computing, Computational Science, National Renewable Energy Laboratory
The GW approximation is a highly accurate ab-initio quantum mechanical technique for understanding the electronic structure of solids and molecules. The evaluation of the polarizability within the GW approximation requires a double summation over occupied/virtual state pairs. Thus, information regarding which individual quantum states contribute to the polarizability is encoded in the computation of the quantity. The relative importance of a particular state to the polarizability can be ascertained by examining how the presence or absence of the state affects the computed value of the polarizability. Implementing a mechanism to examine the relative importance of any arbitrary set of states in the calculation of the inverse dielectric and polarizability matrices in the BerkeleyGW package was the purpose of the practicum project. These matrices can be used to obtain properties such as macroscopic dielectric functions and electron energy loss spectra (EELS), and can be used as input for GW calculations of electronic properties and GW-BSE calculations of optical properties. For example, we were able to compute the simulated EELS spectrum of GaN while including all valence bands, including only Gallium d-bands, and including no Gallium d-bands in the calculation to study how the plasmon peak in the spectrum evolves under these changes. This allowed us to systematically examine the contribution of the Gallium d-bands to the GaN plasmon mode.
Machine Learning to Enable Chemically Informative Atomistic Simulation in Complex Environments
K. Grace Johnson, Stanford University
Practicum Year: 2019
Practicum Supervisor: Ross Larsen, Senior Scientist, Computational Science Center, National Renewable Energy Laboratory
Understanding the properties and function of materials of interest for energy research requires modeling large, hierarchical systems in atomistic detail. Currently, we do not have computational methods that can handle both this scale and fidelity. Ab initio molecular dynamics (AIMD) can describe materials chemistry (including polarizability and the making/breaking of bonds) because the system is evolved in time according to forces solved quantum mechanically (plane-wave DFT). This is crucial for characterizing material properties such as catalysis and the complex chemistry at interfaces. However, AIMD simulations are costly and therefore severely limited by system size and timescale of the dynamics. Classical MD, on the other hand, can model much larger systems on longer timescales. Because MD evolves according to a parametrized force field, however, it cannot describe complex, changing chemical phenomena. A new method that combines the speed of classical MD with the fidelity of AIMD is necessary. We are developing chemically aware force fields for modeling materials dynamics using machine learning (ML) techniques. Our first goal is to predict atomic charges in hybrid perovskite solar cell (HPSC) materials from descriptors (bispectrum coefficients) which characterize the environment around each atom. The descriptors can be readily calculated for a given configuration, while the true atomic charges are obtained via an expensive AIMD calculation. We explore several supervised learning techniques to predict the atomic charges, including support vector regression and neural networks.
Visualizing Dallas/Fort Worth Airport models and simulations
Melissa Queen, University of Washington
Practicum Year: 2019
Practicum Supervisor: Kristi Potter, , Data Visualization, National Renewable Energy Laboratory
In 2018 the DOE launched the ATHENA project: Advancing Transportation Hubs’ Efficiency Using Novel Analytics. The goal of this project is to build a model of airports' traffic (both passenger and freight, and across both air and land) that will allow possible changes and optimizations to be simulated. This will allow long-term planning to take into account the impact of certain decisions, potentially reducing energy consumption and cost. When I joined the team in June 2019 modeling and simulations of the Dallas/Fort Worth International Airport was already underway. For my project, I built customized visualizations of model performance and well as airport simulation results. We were immediately able to draw insights from such visualizations and improve model and simulation accuracy.
Matrix Completion for Low-Observability Voltage Estimation
Priya Donti, Carnegie Mellon University
Practicum Year: 2018
Practicum Supervisor: Andrey Bernstein, Senior Engineer, Power Systems Engineering Center, National Renewable Energy Laboratory
With the rising penetration of distributed energy resources, distribution system ancillary services and enabling techniques such as state estimation have become essential to distribution system operation. However, traditional state estimation techniques have difficulty coping with (1) the low-observability conditions often present on the distribution system due to the paucity of sensor measurements, and (2) the noisy nature of the measurements that do exist. To address these limitations, we propose a state estimation algorithm that employs matrix completion (a tool for estimating missing values in low rank matrices) augmented with noise-resilient power flow constraints. We empirically evaluate our method on the 33 and 39 bus IEEE test systems. We find that our method provides acceptable state estimation performance (within 5%) in low-rank regimes where traditional state estimation algorithms may not be able to operate. Our method additionally outperforms the state-of-the-art algorithm of weighted least squares with pseudo-measurements over a wide range of data availability scenarios.
PREDICTING CRYSTAL BAND GAPS USING MESSAGE PASSING NEURAL NETWORKS
Harshil Kamdar, Harvard University
Practicum Year: 2018
Practicum Supervisor: Caleb Phillips, Data Scientist, Computational Science Center, National Renewable Energy Laboratory
The discovery of new materials for solar panels is considered one of the biggest challenges in materials science and renewable energy. There have been several unique databases that have been compiled over the years that use Density Functional Theory (DFT) to calculate a wide variety of bulk properties of crystalline compounds to help with this lofty goal. One of these properties, the bandgap, is particularly important in the context of photovoltaics. However, DFT calculations are very computationally expensive. In this project, we utilized a new flavor of neural networks built to operate on graphs to predict the bandgap of crystals using just the structure of that crystal and the properties of the atoms in that crystal. We applied our model to the NREL Materials Database and obtained a promising root-mean-square error of 0.21 eV and calculation times orders of magnitudes lower than DFT calculated bandgaps. Furthermore, we explore the failure modes of our model and where improvements could be made. This work could be used in the future on experimental datasets to explore new crystal structures and help with material discovery for solar cells.
Distributed Algorithms to Avoid Tragedies of the Commons in Smart Meter Collectives
Richard Barnes, University of California, Berkeley
Practicum Year: 2016
Practicum Supervisor: Wesley Jones, Group Manager/Senior Scientist, Modeling & Simulation Group, National Renewable Energy Laboratory
In the future, it is likely that our houses will adjust when our heaters, refridgerators, and electrical vehicles are active in reponse to real-time electrical prices. However, if a large group of houses make the same decisions, insufficient power may be available, and the power grid overloaded. The purpose of this project was to develop and simulate distributed algorithms which maximize individual benefits while minimizing such negatives outcomes, which may emerge from consumers' collective behavior. As discussed in various responses below, the foregoing project did not succeed as planned. As a fallback, I developed new modules for GridLAB-D, which is the PNNL software underlying NREL's grid simulation software. This entailed developing an understanding of cosimulation, discrete event modeling, and discrete-continuous time hybrid models.
Power Prediction and Scheduling
Hilary Egan, University of Colorado
Practicum Year: 2016
Practicum Supervisor: Caleb Phillips, Data Scientist, Computational Sciences, National Renewable Energy Laboratory
As supercomputers reach the exascale, power consumption is quickly becoming a limiting factor. Requiring leadership-class HPC systems to have dedicated power plants is clearly not a sustainable path. Smaller supercomputing facilities and data centers are also affected by power constraints, namely through surcharges due to exceedingly high peak power draws. To better understand power use in HPC workloads, the National Renewable Energy Laboratory (NREL) supercomputing facility has tracked the power used by each node in 10 second intervals over the course of the past year as well as a record of all jobs submitted during this time period and their metadata. Using these data, we have performed cluster analysis and dimension reduction with the goal of understanding typical power characteristics and patterns across a wide variety of applications. This analysis informs our efforts to predict, a priori, the power that will be used by a given job. These power predictions are integral for designing new power-aware schedulers and workload managers. To evaluate the efficacy of these predictions in practice, we utilize simulations of the supercomputing job schedule to determine if peak power use could be reduced. We found that with a minimal delay in mean job wait times the overall variability in total system power use can be mitigated.
Simulating Heliostat Degradation for Concentrated Solar Power
Chelsea Harris, University of California, Berkeley
Practicum Year: 2015
Practicum Supervisor: Ross Larsen, Senior Scientist, Computational Science, National Renewable Energy Laboratory
Concentrated solar power (CSP) is a form of renewable energy. Unlike photovoltaic cells that most people are familiar with, this form of solar energy is based on focusing sunlight using specialized mirrors called heliostats; the sunlight heats water that powers a steam turbine. At the National Renewable Energy Laboratory, the PREDICTS team is developing a way to simulated the chemistry and physics of heliostats to understand the degradation of these mirrors with time.
Radical Understanding of Lignin Biosynthesis
Heather Mayes, Northwestern University
Practicum Year: 2012
Practicum Supervisor: Mark Nimlos, Principal Scientist, National Bioenergy Center, National Renewable Energy Laboratory
We used Car-Parrinello Molecular Dynamics (CPMD) to study the reaction pathways involved in creation of lignin, one of the primary components of plant biomass. CPMD allows investigation of time-dependent phenomena while still explicitly treating electrons. It can thus be used for applications in which electrons are important, such as bond breaking and formation, which cannot be done with classical molecular dynamics.
Studies of Oxidized Cellulose Decrystalization and Binding Affinity
Joshua Vermaas, University of Illinois at Urbana-Champaign
Practicum Year: 2012
Practicum Supervisor: Gregg Beckham, Senior Engineer, National Bioenergy Center, National Renewable Energy Laboratory
The overarching project is learning how to degrade cellulose into simpler sugars suitable for making biofuels. Specifically, we were looking at the impact of cellulose oxidation on the decrystallization and binding affinity of cellulose. Four different oxidized products were studied, two each from the reducing and non-reducing ends, using a native-contact reaction coordinate to describe the decrystallization progression and the resulting free-energy profile. The binding affinities for the oxidized species to two different cellulose-degrading enzymes was calculated via thermodynamic integration calculations.