APS March Meeting 2020

Pre-Meeting Events


Morning Tutorials
Sunday, March 1
8:30 a.m. - 12:30 p.m.

T1 Density Functional Theory

T1: Density Functional Theory - Room TBD

Who Should Attend

Graduate students, post-docs, and other scientists interested in learning about the essential elements of Density Functional Theory, both in its ground-state and time-dependent formulations. The tutorial talks will be very pedagogical, covering the fundamentals of the theory and a few applications, latest developments, and unsolved questions. This tutorial will be a good introduction for those who are planning to attend the symposium “Density Functional Theory and Beyond” or other focused sessions at this APS meeting.

Tutorial Description

Density Functional Theory (DFT) provides a practical route for calculating the electronic structure of matter at all levels of aggregation. Five decades after its inception, it is now routinely used in many fields of research, from materials engineering to drug design. Time-dependent Density Functional Theory (TDDFT) has extended the success of DFT to time-dependent phenomena and excitations. Most applications are carried out in the linear-response regime to describe excitation and emission spectra, but the theory is applicable to a much broader class of problems, including strong-field phenomena, attosecond control of electron dynamics, nanoscale transport, and non-adiabatic dynamics of coupled electron-nuclear systems. The tutorial will provide an introduction to the basic formalism of DFT and TDDFT, an overview of state-of-the-art functionals and applications, and a discussion of the most pressing and challenging open questions.


  • DFT: Basic theorems of ground-state DFT, with simple examples; exchange-correlation functionals and exact conditions such as scaling, self-interaction, and derivative discontinuities; exact exchange and beyond; the Jacob’s ladder of Density Functional approximations.

  • TDDFT: Basic theorems of TDDFT, with simple examples; survey of time-dependent phenomena; memory dependence; linear response and excitation energies; optical processes in materials; multiple and charge-transfer excitations; strong-field processes; non-adiabatic electron-nuclear dynamics.


Neepa Maitra, Rutgers University at Newark


  • John Perdew, Temple University

  • Lucia Reining, Ecole Polytechnique, France

  • Carsten Ullrich, University of Missouri-Columbia

  • Adam Wasserman, Purdue University

T2 Active Learning and AI for Computational and Autonomous Experiments

T2: Active Learning and AI for Computational and Autonomous Experiments - Room TBD

Who Should Attend

Graduate students, post-docs, and other scientists interested in learning about how to decide which measurements are best to perform given limited resources. This workshop will be hands-on and features real-world examples.

Tutorial Description

During the course of research, we are often faced with the question of which experimental or computational measurement to perform next. When these measurements are expensive or time-consuming to perform, this question becomes critical. Active learning is a technique in machine learning that provides a framework to systematically answer that question by finding an optimal set of measurements to maximize knowledge gained (given the results already in hand), thus controlling the cost. Machine Learning can then be placed in control of materials synthesis and characterization in a closed-loop system, with active learning providing experiment design on every iteration. Recent examples of autonomous systems include mapping out phase diagrams as well as choosing which computational experiments to perform. In this tutorial, we will introduce the theory of Gaussian processes and active learning using open source python libraries. Here, Gaussian processes are used to learn from prior data and make predictions for the results of future experiments, with associated uncertainty. Active learning then utilizes these predictions to determine the next best experiment to perform. Hands-on exercises will be provided based on actual use cases.


  • Gaussian Processes

  • Active Learning in Experimental Systems

  • Active Learning of Molecular Dynamics Force Fields


Mohammad Soltanieh-ha


  • Aaron Gilad Kusne, NIST
  • Daniel Samarov, NIST
  • Boris Kozinsky, Harvard University
  • Jonathan Vandermause, Harvard University
T3 Noisy Quantum Devices

T3: Noisy Quantum Devices - Room TBD

Who Should Attend

Graduate students, post-docs, and other scientists interested in quantum computing and the limitations on it due to noise. The tutorial talks will be pedagogical, describing the theoretical foundations and tools of the field both in theory and experiment. Latest developments and open questions will also be prominently featured.

Tutorial Description

The last few years have seen impressive progress in the construction of multi-qubit quantum devices towards the ultimate goal of a fault-tolerant universal quantum computer. However, until we achieve this final goal, we must learn to take advantage of large, but rather noisy devices. Since there are multiple technologies being explored, such as superconducting circuit, ions and spins, we must also learn how to devise tools that are agnostic to the underlying implementation. In this regime there are two main challenges. One challenge is how to characterize and (hopefully) improve errors due to this noise. The other challenge is how to harness the quantum behavior available despite this noisy environment. There are many proposals and some demonstrations for how to obtain a quantum advantage in such systems. These lectures will give an introduction to the types of noise on these devices, typical characterization methods, and applications.


  • Experimental realizations: Spintronics, superconducting qubits, cold gas atom and trapped ion systems, noise considerations.

  • Theory: Quantum computing basics, error minimization and correction, limitations on complexity, fault tolerance.


David McKay, IBM


  • Abhinav Kandala, IBM

  • Robin Blume-Kahout, Sandia National Lab

  • Scott Aaronson, University of Texas at Austin

  • Guido Pagano, Rice University

Afternoon Tutorials
Sunday, March 1

1:30 p.m. - 5:30 p.m.

T4 Data analysis and modern visualization

T4: Data analysis and modern visualization - Room TBD

Who Should Attend

Analysis and visualization of scientific data is crucial for researchers to illustrate results and to communicate insight to specialized as well as broad audiences. This tutorial aims at graduate students, post-docs, faculty, and other scientists interested in learning about approaches to analyze and visualize scientific data using cutting-edge approaches. The tutorial talks will be very pedagogical in describing approaches as well as practical techniques used to plan analysis and visualization, use various software packages to create visualizations of different types of data sets, and tie these results together in videos or animations. Latest developments and open questions will naturally be integrated into each topic and equip attendees with practical and exciting skills that are transferable to their daily research activities.

Tutorial Description

Visualization is an integral part of the scientific process. As an excellent communication tool used by researchers, it is crucial to make complicated scientific data and relations accessible and understandable. This tutorial will train attendees to expand and develop their visualization skills by providing a "from the ground up" understanding of visualization and its utility in error diagnostic and exploration of data for scientific insight. When used effectively, visualization can provide a complementary and effective toolset for data analysis, which is one of the most challenging problems in many scientific domains. We plan to bridge these gaps by providing attendees with fundamental visualization concepts, execution tools, customization, and usage examples. The tutorial will cover different viewpoints, including (1) a rapid introduction to fundamental visualization concepts, (2) an assay of visualization techniques available accompanied by example application scenarios centered around the VisIt software, (3) scientific data visualization using the Blender software, and (4) combination of visualization of data into advanced animations and videos using a full programming language such as Matlab or Python.


Fundamentals, Data analysis, VisIt and Blender software, Using a full programming language


André Schleife, Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign


  • Roberto Reynel Sisneros, National Center for Supercomputing Applications

  • David Pugmire, Oak Ridge National Lab

  • Brian Kent, National Radio Astronomy Observatory

  • Colin Ophus, Lawrence Berkeley National Lab

T5 Medical Metrology

T5: Medical Metrology - Room TBD

Who Should Attend  

Physicists in academia and industry from GMED, DCOMP, FIAP, GIMS, GDS, DAMOP, DLS, and other APS units interested in applications of physics in medicine. Researchers without prior experience are welcome.

Course Description

Medicine is becoming increasingly quantitative. Continuing innovation in medical sensors has greatly expanded the range of physical variables that can be accurately measured to probe various aspects of the structure, function, and composition of human body. The analysis of the resulting rich streams of information is enabled by the rapidly developing data science techniques, yielding sophisticated quantitative descriptors of disease and therapeutic response (“quantitative biomarkers”). The trend towards measurement-driven medicine opens exciting opportunities for physicists to contribute their expertise in metrology. This tutorial will cover (i) recent advances in medical sensor technologies (How and what do we measure?), (ii) the discovery and validation of quantitative biomarkers (How do we establish whether a given physical measurement is a biomarker?), and (iii) data analysis techniques that enable clinical decision support based on complex, multi-dimensional biomedical measurements (How do we combine multiple biomarkers into a statistical inference framework?).

Topics to be covered:

  • Novel sensors for biomedical measurements: The presentations will focus on recent advances in devices for biomedical metrology, in particular optical and magnetic properties of tissues. Other measurement modalities will also be briefly introduced.

  • Discovery and validation of quantitative biomarkers: We will use examples from oncology and neurology to present a rigorous review of how physical measurements are developed into quantitative biomarkers, including statistical techniques used for biomarker validation, e.g. with respect to their sensitivity to measurement conditions.

  • Data analysis and computational modeling for quantitative medicine: We will present the statistical and computational techniques that are used to develop quantitative clinical decision support based on the complex, multi-dimensional, multi-modality measurement data provided by modern medical devices. This emerging field is a fertile ground for new insights from physicists interested in data science and computational modeling.

The tutorial will also be accompanied by a tour of local imaging and radiotherapy facilities.


Wojtek Zbijewski, (Johns Hopkins University)


  • Ed Jackson, (University of Wisconsin)

  • Kathryn Keenan or Stephen Russek, (NIST)

  • Matija Milanic, (University of Ljubljana)

  • Robert Jeraj, (University of Wisconsin)

T6 Quantum Metrology

T6: Quantum Metrology - Room TBD

Who Should Attend

Graduate students, post-docs, and other scientists interested in learning about the exciting new area of quantum sensing and metrology. The tutorial talks will be very pedagogical, describing the theoretical foundations and tools of the field, the techniques for developing and operating a diverse range of quantum sensor devices, as well as their many applications. Latest developments and open questions will also be prominently featured.

Tutorial Description

Quantum sensing and metrology encompasses a class of techniques and devices that exploit quantum properties such as coherent superposition, wave-particle duality, and entanglement to detect weak or nanoscale signals arising, e.g., from electromagnetic fields, temperature, gravitational gradients, and pressure. As their behavior is tied to physical constants and symmetries, quantum sensors can achieve accuracy, repeatability, and precision approaching fundamental limits. As a result, these devices have shown utility in a wide range of applications spanning across the physical and life sciences —leading to a new generation of real-world technologies with exciting potential. Example quantum sensing platforms to be discussed include atom interferometers; optically-active quantum defects in solids, which have electronic and nuclear spin and can be deployed both for nanoscale sensing with single defects and for bulk sensing with dense ensembles of defects; and atomic vapors constrained in micro-machined (“chip-scale”) chambers. Applications being pursued include searches for dark matter; mid-frequency detectors of gravitational waves; probing of novel 2D materials; biomedical diagnostics; NMR of single cells and molecules; neuroscience; and vehicle navigation in the absence of GPS signals. The tutorial will provide an introduction to the principles, techniques, and operating regimes of different quantum sensing modalities, emerging areas of application, and key open questions in the field.


  • Theory: Principles of atom interferometry; spin and optical physics of quantum defects in high-bandgap solids such as diamond, as well as alkali atoms used in atomic vapor sensors; and figures of merit for quantum sensor performance, including sources of decoherence and fundamental measurement limits.

  • Experimental principles: Sensor operational techniques, measurement protocols, systematic effects, and current and projected performance metrics for each class of quantum sensor.

  • Applications: Survey of diverse applications in the physical and life sciences for each class of quantum sensor.


Ronald Walsworth, University of Maryland and Harvard-Smithsonian Center for Astrophysics

Speakers (Subject to confirmation)

  • Jason Hogan, Stanford University

  • Toeno van der Sar, Delft University of Technology

  • Jennifer Schloss, MIT-Lincoln Lab

  • John Kitching, NIST-Boulder

Registration Fee per Tutorial

  • Regular Attendee: $125
  • Students: $65

Registration Instructions

Sign up for tutorials when you register for the meeting.

Short Courses

Sign up for short courses when you register for the meeting.

DPOLY Short Course - TBD
Saturday, February 29, 1:00 p.m. - 6:00 p.m.
Sunday, March 1, 8:15 a.m. - 5:30 p.m.

Machine Learning for Polymer Physicists

Machine Learning for Polymer Physicists


Recent developments in machine learning and related data-driven approaches have created a new paradigm for approaching scientific research. The field of polymer physics has seen important applications in the design of experiments, analysis of scattering data, prediction of molecular properties, and identification of important structural and dynamic patterns. Additionally, the use of high throughput computational and experimental techniques promises to increase the amount of data available to polymer physicists and presents new opportunities for discovery. This day and a half short course will provide an essential introduction to machine learning and data analytics as relevant to polymer physicists, while also showcasing recent advances by leaders in the field. Topics covered will include data capture, design of experiments, varying levels of data quality, model building, optimization and general analysis of both experimental and computational data. Attendees will leave with a sound basis in key algorithmic concepts including when those algorithms are appropriate, an understanding of the state-of-the-art applications, and a foundational understanding of how to incorporate machine learning and data science into their current research.

Who should attend?

The workshop is appropriate for polymer and soft materials researchers at all levels who wish to integrate machine learning techniques into their work. The short course will be particularly useful for people who have not received formal data science training, but appreciate the power of data science to augment and extend traditional techniques. While aimed toward early-career researchers, including graduate students, postdocs, and early career PIs there will be topics of interest for researchers at all career levels from both computational and experimental groups.

Organizers: Debbie Audus, NIST; Jonathan K. Whitmer, University of Notre Dame

DSOFT Short Course - Room TBD
Sunday, March 1
9:00 a.m. - 5:00 p.m.

DNA Nanotechnology Meets Soft Matter

DNA Nanotechnology Meets Soft Matter

I. Course Description

DNA Nanotechnology is a rapidly growing technology impacting many sectors of science and technology, and in particular it is increasingly being harnessed to tackle new and frontier challenges in soft matter science. This course aims to introduce junior researchers in the soft matter field about the elements of DNA Nanotechnology and the state-of-the-art in how it is being applied to address problems in soft matter science and engineering. The course will cover topics outlining the physical chemistry of DNA, the design and assembly of DNA based nano-structures such as DNA tiles and DNA origami, the programming of assembly using DNA, DNA based hyrdogels, and DNA-functionalized colloids.

II. Who Should Attend

Students and researchers new to the field are strongly encouraged to attend. Speakers will survey current tools available for soft matter physics, both experimental and theoretical research, using DNA nanotechnology.

GSNP Short Course - Room TBD
Sunday, March 1
8:00 a.m. - 5:30 p.m.

Machine Learning in Statistical and Nonlinear Physics

Machine Learning in Statistical and Nonlinear Physics

Machine learning tools play a growing role in progress in many fields of science. The complex systems addressed by statistical and nonlinear physics are often well suited to exploration with machine learning. In turn, the physics approaches for studying these systems has lead to improvements in machine learning in a new area called the "Physics of Artificial Intelligence." This workshop will include pedagogical talks by experts in our field as well as hands-on computational activities.

Michael Kirby, Colorado State University, Fort Collins
Juan Restrepo, University of Colorado at Boulder
Aaron Gilad Kusne, National Institutes of Standards and Technology Pathak, Lawrence Berkeley Laboratory
Maissara Raissi, Brown University

Daniel Lathrop, University of Maryland
Dvora Perahia, Clemson University

GDS Short Course - Room TBD
Sunday, March 1
8:00 a.m. - 5:30 p.m.

Deep Learning for Image Processing Applications

Deep Learning for Image Processing Applications

Artificial Intelligence (AI) is a collection of advanced technologies and mathematical methodologies that allows machines to think and act through sensing, comprehending, acting, and learning. This half-century-old field of research has recently garnered renewed attention, partly by several breakthroughs in the design of new AI algorithms, in particular, for image analysis, but also because of the new computing technologies that make the calibration of AI models feasible on personal computers, thus allowing their wide-spread usage by individual researchers in diverse fields of science and engineering. This hands-on workshop will be investigating how deep learning techniques for image analysis can be adapted or developed to address topics and challenges in image analysis and material science. Some of these topics are hyperspectral data analysis in imaging, deep learning applications in active imaging (denoising,drift correction, and deep-learning-based feature extraction), materials discovery, and learning physics from imaging data of mesoscopic and stochastic systems.

Big data and machine learning in theory (Alexander Tropsha, University of North Carolina
Machine learning and hyperspectral data analysis in imaging (Sergei Kalinin, ORNL)
Applications of deep learning for active imaging: denoising, drift correction, and deep learning-based feature extraction (Maxim Ziatdinov, ORNL)
ML in materials discovery (Jason Hattrick-Simpers, NIST)
Learning physics from imaging data: mesoscopic and stochastic systems (Rama Vasudevan, ORNL)
Automatic experimentation (Benji Maryama, Air Force Research Laboratory)

Who Should Attend?
Graduate students, post-docs, and other scientists interested in learning about deep learning and its applications in image processing. This workshop will be hands-on and features real-world examples.

Sergei Kalinin, Oak Ridge National Laboratory
Amir Shahmoradi, University of Texas at Arlington
Mohammad Soltanieh-ha, Boston University