Integrating renewable sources on the North American grid: experiences, problems and trends
Electrical Engineering, Tufts University
Friday, February 28, 12:00 PM, C215 ESC
Abstract: This talk aims to outline some salient features of the US renewable energy integration. The electricity prices in the US are roughly half of those in Europe, there is no long-term national renewable energy policy, and the legal landscape is complicated by the existence of federal, state and local regulations. We propose to analyze energy systems as multi-layer structures involving flows of material, energy information and capital. Renewable energy has been tasked with improving sustainability, namely the input-output map at the material flow layer. Recently, its affordability has improved (the capital flow map), especially in the case of wind and solar. We argue that resilience offers a natural way to couple and characterize all 4 layers in a multi-criterial fashion. We review relevant electrical technologies, such as power electronics and storage, and briefly discuss promising concepts such as cyber-physical systems and energy hubs. Finally, we briefly outline near-future developments in the case of off-shore wind.
Biographical Sketch: Alex M. Stankovic obtained the Dipl. Ing. Degree from the University of Belgrade, Yugoslavia in 1982, the M.S. degree from the same institution in 1986, and the Ph.D. degree from Massachusetts Institute of Technology in 1993, all in electrical engineering. He serves as the A.H. Howell Professor at Tufts University; he was with Northeastern University, Boston 1993-2010. He is a Fellow of IEEE and serves as an Associate Editor for Annual Reviews in Control. He previously served IEEE Transactions on Power Systems, on Smart Grid and on Control System Technology in the same capacity (1996-2018). He has held visiting positions at the United Technologies Research Center (sabbaticals in 2000 and 2007) and at L'Universite de Paris-Sud and Supelec (in 2004). He is a co-editor of book series on Power Electronics and Power Systems for Springer.
Superconducting Qubits: from Rabi Oscillations to Real-World Applications
IBM Watson Research Center
Friday, March 6, 12:00 PM, C215 ESC
Abstract: The outline of my presentation will be as follows:
• Design and properties
• Control and performance
• Cloud-based quantum systems
• Programming today’s quantum computers
• Identifying early use cases
Biographical Sketch: Dr. Brent A. Wacaser obtained his B.S. and M.S. in Physics from Brigham Young University in 2001 and 2002, respectively. He then obtained his PhD in Physics and Nanotechnology from Lund University, Sweden in 2007. Also in 2007, Brent completed a six week graduate student sabbatical on a visiting scientist EU transnational access grant at Weizmann Institute of Science, Rehovot, Israel. He then Joined IBM Research as a Post Doctoral researcher and in 2011 was hired full time as a Research staff member. At IBM Brent has worked on nanowire growth and applications for solar cells, he also worked on concentrated solar photovoltaics and worked as an international assignee to King Abdullah City for Science and Technology (KACST), Saudi Arabia working as a mentor and liaison for a joint IBM / KACST project. Brent has also worked in a team focusing on incorporating III-V semiconductor materials into CMOS technology and is currently working on IBM quantum computing team. Over the course of his career as a student, post-doc, and researcher he has published over 40 papers in prestigious refereed journals and is an inventor on over 25 patents. He has participated in over 10 conferences as a contributing and invited speaker. He has also helped organize and conduct conferences. His novel ideas and work have improved understanding and opened doors to new applications in the fields of crystal growth, nanotechnology, PV and CPV research, III-V material incorporation in CMOS, and Quantum Computing.
NASA Langley Research Center
Friday, March 13, 12:00 PM, C215 ESC
Physics & Astronomy, Brigham Young University
Friday, March 27, 12:00 PM, C215 ESC
Chemistry, Brigham Young University
Friday, April 3, 12:00 PM, C215 ESC
Machine-learning for materials and physics discovery through symbolic regression and kernel methods
Materials Science & Engineering, University of Florida
Friday, April 10, 12:00 PM, C215 ESC
Machine-learning can provide surrogate models that aid the search for new materials and new analytic equations that describe physical relationships. We will present an example for each type of learning: (i) the learning of surrogate kernel methods for the accelerated exploration of energy landscapes and (ii) the data-driven discovery of the functional form of the superconducting critical temperature.
First, we present our kernel approach to developing surrogate machine learning models for energy prediction. Using structurally and compositionally diverse materials generated with our genetic algorithm package GASP and their formation energies from density functional theory, we train interatomic potentials using support vector regression. We show that radial and angular distribution functions can efficiently encode relevant physical information into machine-readable inputs and obey required constraints. We demonstrate how augmenting the training data with local energies improves model performance. These models can filter low-value candidates, reducing the computational cost of the genetic algorithm by eliminating materials with a high probability of having higher energy .
Second, predicting the critical temperature, Tc, of superconductors is a notoriously difficult task, even for electron-phonon systems. We build on earlier efforts by McMillan and Allen and Dynes to model Tc from various measures of the phonon spectrum and the electron-phonon interaction by using machine learning algorithms. Specifically, we use symbolic regression building on the SISSO framework to identify a new, physically interpretable equation for Tc as a function of a small number of physical quantities. We show that our model, trained using the relatively small data tested by Allen and Dynes, improves upon the Allen-Dynes fit and can reasonably generalize to superconducting materials with higher Tc such as H3S and LaH10. By incorporating physical insights and constraints into a data-driven approach, we demonstrate that machine-learning methods can identify the relevant physical quantities and obtain predictive equations using small but high-quality datasets .
 S. Honrao, B. E. Anthonio, R. Ramanathan, J. J. Gabriel, and R. G. Hennig, Comp. Mater. Sci. 158, 414 (2019).  S. R. Xie, G. R. Stewart, J. J. Hamlin, P. J. Hirschfeld, and R. G. Hennig, arXiv:1905.06780 (2019).
Biographical Sketch: Professor Hennig received his Diploma in Physics at the University of Göttingen in 1996 and his Ph.D. in Physics from Washington University in St. Louis in 2000. After working as a postdoctoral researcher and research scientist at Ohio State University, he joined the faculty of the Department of Materials Science and Engineering at Cornell in 2006 as an Assistant Professor. In 2014 he moved to the University of Florida where he is the Alumni Professor of Materials Science and Engineering and the Associate Director, Quantum Theory Project.
We welcome anyone who wish to attend, and typically serve refreshments ten minutes before the colloquium begins. Speakers generally keep their presentation accessible to undergraduate physics students.