Prof. Philip Mannheim, University of Connecticut

The Accelerating Universe

I will describe some of the background that led to the award of the Nobel prize to Dr. Adam Riess, who will be our 2024 Katzenstein speaker on November 15.

Prof. Matthew Guthrie, University of Connecticut;

A New Era for Connecticut’s Oldest Planetarium: Historic Roots to Modern Revival

The UConn Planetarium, built in 1954, has long been a central resource for astronomy education and outreach at the University of Connecticut. In this talk, I will present an overview of the planetarium’s historical roots at UConn, its significance in the community, and the extensive renovations we have completed to bring this important facility back to life. After years of disuse, the planetarium has been fully upgraded with modern technology and will officially reopen on November 1st, immediately following this colloquium.

Our efforts to restore the planetarium are guided by the legacy of Dr. Cynthia Peterson, UConn’s first woman physics faculty member and a pioneer in science education. The planetarium now officially bears her name as the Cynthia Wyeth Peterson Memorial Planetarium, in honor of her decades of dedication to astronomy outreach. Following my presentation, Nora Berrah and Celeste Peterson will speak about Dr. Peterson’s life and achievements - how her contributions to UConn and the wider scientific community continue to resonate today.

Prof. Philip Kim, Harvard University

Searching for Anyon in Quantum Materials

The search for anyons, quasiparticles with fractional charge and exotic exchange statistics, has inspired decades of condensed matter research. Moreover, it has been predicted that exchange braiding of these particles, especially non-abelian anyons, can produce topologically protected logic operations that can serve as building blocks for fault-tolerant quantum computing. In this talk, I will discuss the progress of research on two quantum materials platforms to realize these exotic particles. In the first example, we will discuss anyons arising in fractional quantum Hall (FQH) effects, using quantum Hall interferometers for direct observation of the anyon braiding phase around a confined cavity. In the second example, we will discuss our recent experimental efforts to realize non-abelian anyons in proximitized topological insulator surfaces by controlled manipulation of magnetic vortices containing non-abelian anyons.

Prof. Jun Ye, University of Colorado and JILA

Title and abstract TBA

Prof. Mingda Li, Nuclear Science and Engineering, MIT

Exploring Potential Roles of Machine Learning in Quantum Materials Research

In recent years, machine learning has achieved great success in chemistry and materials science, but quantum materials face unique challenges. These include the scarcity of data (volume challenge), high dimensionality and computational costs (complexity challenge), elusive experimental signatures (experimental challenge), and unreliable ground truth (validation challenge).

In this Physics Colloquium, we present our recent efforts to support the study of quantum materials with machine learning. For scenarios with high data volumes, such as density-functional-theory (DFT) level studies with weak correlation, machine learning can predict lower-dimensional properties. We introduce a convolutional neural network classifier predicting band topology class based on X-ray absorption (XAS) signals [1]. This approach can also be applied to experimental data, demonstrated by an autoencoder-based protocol to study the magnetic proximity effect with polarized neutron reflectometry, improving fitting resolution [2].

For lower data volumes due to higher computational costs, incorporating symmetry into neural networks can reduce data volume needs. Using the O(3) Euclidean neural network, we predict phonon density-of-states [3], dielectric functions [4], and quantum weight [5] directly from crystal structures. Machine learning without data can also be performed by using differential equations as constraints [5].

For high output dimensions and low input data volumes, such as phonon dispersion relations, we introduce additional approaches like virtual nodes in a graph neural network [6], showing improved efficiency compared to machine-learning potential without losing accuracy.

To address unreliable ground truth, we use machine learning to distinguish Majorana zero modes in scanning tunneling spectroscopy for topological quantum computation [7]. For cases like quantum spin liquids, where experimental signatures are unclear and computational costs are high, we generate materials with potential geometrical frustration. Our latest work, SCIGEN, produces eight million materials belonging to Archimedean lattices, with over 50% passing DFT stability checks after pre-screening [8].

Despite progress, applying machine learning to quantum materials is still in its infancy. We reflect on the out-of-distribution problem, aiming to generate genuine surprises and new features rather than merely recognizing patterns. Additionally, we must address accuracy limitations in many machine learning approaches, especially with complex quantum systems and phase diagram studies.

[1] “Machine learning spectral indicators of topology,” Advanced Materials 34, 202204113 (2022).

[2] “Elucidating proximity magnetism through polarized neutron reflectometry and machine learning,” Applied Physics Review 9, 011421 (2022).

[3] “Direct prediction of phonon density of states with Euclidean neural networks,” Advanced Science 8, 2004214 (2021).

[4] “Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure,” arXiv:2406.16654.

[5] “Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning,” Advanced Materials 35, 2206997 (2023).

[6] “Virtual Node Graph Neural Network for Full Phonon Prediction,” Nature Computational Science 4, 522 (2024).

[7] “Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements,” Matter 7, 2507 (2024).

[8] “Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates,” arXiv:2407.04557.

Prof.. Reina Maruyama,Yale University

What is dark matter?

Astrophysical observations give overwhelming evidence for the existence of dark matter. Several theoretical particles have been proposed as dark matter candidates, including weakly interacting massive particles (WIMPs), axions, and, more recently, their much lighter counterparts. However, there has yet to be a definitive detection of dark matter. For years, one group, the DAMA collaboration, has asserted that they observe a dark matter-induced annual modulation signal in their NaI(Tl)-based detectors. Their observations are inconsistent with those from other direct detection dark matter experiments under most assumptions of dark matter. In this talk, I will describe how I came to work on this topic and the debate’s current status, the worldwide experimental effort to test this extraordinary claim, and our progress toward resolving the current stalemate in the field.

Note: The pre-colloquium reception will be 3-4pm in the Gant Light Court

Prof. Cassandra Paul (San Jose State University)

Title and abstract (TBD)

Contact: Prof. Erin Scanlon

Remote talk (details forthcoming)

TITLE:

Quantum acoustics and the physics of the strange metals

ABSTRACT:

Quantum acoustics is the analog of quantum optics, with phonons playing the role of photons. The classical fields (electromagnetic, acoustic) are reached by virtue of coherent states in both cases. Quantum acoustics leads to two time dependent, interacting wave fields, one lattice, one quantum. The electron diffuses at a Planckian rate, independent of electron-lattice coupling and temperature, and the calculated resistivity is linear in temperature. Mott-Ioffe-Regel and Drude peak mysteries are also resolved. A rather different carrier transport scenario emerges for the strange metals.

Dr. Nick Hutzler, California Institute of Technology

UConn Physics Colloquium

Title and abstract: TBD

Contact: Profs. Tom Blum and Dan McCarron

Dr. James Cryan, SLAC National Lab (UConn Physics Colloquium)

Ultrafast dynamics using X-ray attosecond pulses.

Contact: Prof. Nora Berrah

UConn Physics Colloquium: Dr. Andrew Held

High Power Commercial Laser Markets and Applications

Abstract: Ubiquitous and familiar applications for lasers include telecom data transmission, laser surgery (LASIK), information processing (DVD/Blue Ray), supermarket scanners, laser pointers and a multitude of laser sensing applications (LIDAR, range finders, facial recognition, etc.). Sophisticated laser technology is also well-recognized as a key, enabling research tool.

Perhaps less well known are the “unsung” commercial applications and markets for higher power lasers. Often out of public view, these laser applications drive diverse and massive commercial markets and are supported by extensive industry-based research and development investments. And are generating increasingly abundant STEM based career opportunities.

The presentation will highlight the laser technologies and applications used in materials processing to mark, engrave, cut, and join everything from shoe leather to sheet metal. Also covered are laser applications supporting the manufacturing of microelectronics-based consumer technology, enabling higher performing devices and ever larger displays. The laser technology and developments that support emerging Directed Energy military applications will be also be reviewed.

Bio:

Andrew Held has recently retired as Senior Vice President of Coherent’ s Aerospace and Defense business. Andrew has over 30 years’ experience in General Business Management, Research, Sales and Marketing of lasers and photonics into a broad range of markets and applications. He received his B.S. in Chemistry and Ph.D. in Laser Spectroscopy from the University of Pittsburgh and was an Alexander von Humboldt Research Fellow at the Technical University in Munich.

Dr. Josiah Sinclair, MIT

A new platform for quantum science: programmable arrays of single atoms inside an optical cavity.

Recently, programmable arrays of single atoms have emerged as a leading platform for quantum computing and simulation with experiments demonstrating control over hundreds of atoms [1]. Interfacing an atom array with a high-quality optical cavity promises even greater control and new capabilities. By coupling atoms to an optical cavity, we can more efficiently collect light from each atom improving detection. In addition, an optical cavity can be used to efficiently entangle many atoms in a single step relying on a novel technique called counterfactual carving [2]. I will describe our progress towards the goal of detecting and correcting errors on a register of Rubidium atoms selectively coupled to a large-waist optical cavity. Beyond detecting errors, applying corrections requires real-time feedback, and I will present a simple experiment demonstrating that fast feedback on microsecond timescales can already improve measurement fidelity. Finally, I will describe our accidental realization that we can use our cavity to directly observe collisions between pairs of trapped atoms in real time.