Abstract: TBD

Abstract: TBD

Abstract: TBD

Abstract: TBD

Abstract: TBD

Featured Labs: 

• Prof. Suchi Guha: Experimental Condensed Matter 
• Prof. Ioan Kosztin: Computational Biophysics 
• Prof. Keith Cassidy: Computational Biophysics 

Abstract: The Nobel Prize in Physics for 2024 was awarded to Geoffrey Hinton and John Hopfield for their transformative contributions to the field of machine learning and artificial neural networks. We will explore John Hopfield’s development of the Hopfield network, which introduced a new paradigm for associative memory and pattern recognition. And then examine Geoffrey Hinton’s contributions, including the Boltzmann machine and the backpropagation algorithm, which revolutionized the training of deep neural networks. Their research laid the foundation for modern AI technologies, driving significant advancements in fields such as image and speech recognition, healthcare, and autonomous systems. Time permitting, we will briefly consider the question – Can machines mimic human intelligence?

Abstract: This year’s Nobel Prize in Physics celebrates the transformative contributions of John Hopfield and Geoffrey Hinton, “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” The award illustrates the deep connections between physics and AI. While physics seeks to discover universal laws—ultimately aspiring toward a "theory of everything" that explains phenomena from subatomic particles to galaxies—AI focuses on representing and identifying specific patterns to enhance prediction and decision-making. Hopfield and Hinton's work exemplifies how physics-inspired thinking has shaped neural networks, from the associative memory models rooted in the Ising model to the probabilistic learning framework of restricted Boltzmann machines. Although AI has since evolved toward data-driven approaches, the foundational influence of physics remains evident. Moreover, AI is now driving breakthroughs in chemistry, biology, and physics itself, notably in protein folding and complex differential equations. This ongoing synergy between AI and the physical sciences offers a glimpse into the future, where new opportunities at their intersection will further advance both our understanding of physical phenomena and our ability to solve practical problems through intelligent systems.

Bio: Dong Xu is Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his Ph.D. in Physics from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. Over the past 30 years, he has conducted research in many areas of computational biology and bioinformatics, including single-cell data analysis, protein structure prediction and modeling, protein post-translational modifications, protein localization prediction, computational systems biology, biological information systems, and bioinformatics applications in human, microbes, and plants. His research since 2012 has focused on the interface between bioinformatics and deep learning. He has published more than 500 papers with more than 27,000 citations and an H-index of 84 according to Google Scholar. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.

Abstract: Generally, uniformity in materials is seen as critical to phase order, with disorder and defects being thought to result in lower ordering temperatures and prevention of long-range percolation. However, disorder is an important aspect of many materials systems – from alloys to dilute magnetic semiconductors. It can be used to manipulate superconductivity, magnetic ordering, and design degeneracies. High entropy materials are an evolution of this understanding and work in this field has begun to demonstrate that disorder is a parameter which can drive local microstates into globally ordered behaviors.

In this symposium, theoretical and experimental results exploring the role of disorder in manipulating spin, charge, lattice and electronic order parameters in two classes of single crystal high entropy oxide epitaxial films are discussed. First, in exploring magnetism, electronic structure and valence of the high entropy ABO₃ perovskite La_1-xSr_x(Cr_0.2Mn_0.2Fe_0.2Co_0.2Ni_0.2)O₃. Second, in an experimental realization of extreme A-site cation disorder in (Y_0.2La_0.2Nd_0.2Sm_0.2Gd_0.2)NiO₃, whose parent ternary oxides each have a large range of electronic (metal to insulator transition) and structural phase transition temperatures. These results suggest cation size, spin, and charge variance, such as that accessible only in high entropy oxides, can be critical in the design of next generation electronic, structural, and magnetic materials.

Abstract: Materials with exotic properties have become a key driver in advancing condensed matter and materials physics. Layered materials, in particular, offer exceptional platforms for exploring a wide range of quantum phases and phenomena. The distinct structural characteristics of these compounds allow for significant tunability through chemical or mechanical methods, enabling precise manipulation of electronic states and properties. Moreover, the ability to obtain atomically thin flakes of these materials opens up new possibilities for studying novel properties in reduced dimensions and for creating intricate material designs by constructing various heterostructures. In this talk, I will provide an overview of our recent work on topological and magnetic materials. By leveraging the intertwined lattice, spin, charge, and topology degrees of freedom in these materials, our research explores the engineering of electronic states through lattice and time-reversal symmetry. This manipulation leads to a range of intriguing phenomena, including the emergence of new surface electronic states, potential enhancements in electronic correlations, and insulator-to-metal transitions, among others.

Bio: Jin Hu is an associate professor of physics at the University of Arkansas. He earned his BS degree from the University of Science and Technology of China in 2008 and his PhD degree from Tulane University in 2013. Following the completion of his doctorate, he served as a postdoctoral associate and later as a research assistant professor at Tulane University before joining the University of Arkansas in 2017. He has been working on various quantum material systems including unconventional superconductors, topological materials, 2D materials and published more than 120 papers. He received the DOE Early Career Award in 2021 and the NSF Career Award in 2023. He is part of the NSF MonArk Quantum Foundry and DOE µATOMs EFRC.

Featured Labs: 

• Prof. Pontus Laurell: Theoretical Condensed Matter 
• Prof. Charles Steinhardt: Astrophysics 
• Prof. Deepak Singh: Experimental Condensed Matter 

Abstract: Highly penetrating and non-destructive, with sensitivity to light elements and magnetic fields, neutron beams provide information about the microscopic structure and dynamics of materials that is difficult or impossible to obtain via other techniques.  State-of-the-art neutron measurements require a facility-scale source, such as a nuclear reactor or proton accelerator / target  system, as well as sophisticated, custom-built neutron moderators, delivery systems, and instrumentation.  As such, researcher access to neutron techniques is generally limited to user programs at centralized facilities.  The NIST Center for Neutron Research (NCNR) in Gaithersburg, Maryland hosts one of the world’s premiere neutron instrument suites, but the facility has been shut down since 2021 due to complications from a damaged reactor fuel element.  The NCNR is now in the midst of a major reactor recovery and upgrade project that is scheduled to culminate in a return to scientific operations in early 2026.  I’ll present an overview of this key component of the Nation’s scientific infrastructure, with focus on the recovery process, and recent instrumentation upgrades. 

Abstract: Two-dimensional (2D) kagome lattice metals are interesting because they display flat electronic bands, Dirac points, Van Hove singularity, and can have interplay amongst charge density wave (CDW), magnetic order, and superconductivity. In kagome lattice antiferromagnet FeGe, a short-range CDW order was found deep within an antiferromagnetically ordered state interacting with magnetic order [1]. Surprisingly, the post-growth annealing process of FeGe at 560◦C can suppress the CDW order while annealing at 320◦C induces a long-range CDW order, with the ability to cycle between the states repeatedly by annealing [2]. Here we use transport, neutron scattering, scanning transmission electron microscopy (STEM), and muon spin rotation (μSR) experiments to unveil the microscopic origin of the annealing process and its impact on magneto-transport, CDW, and magnetic properties of FeGe. We find that 560◦C annealing creates germanium vacancies uniformly distributed throughout the FeGe kagome lattice that prevent the formation of Ge-Ge dimers necessary for the CDW order. Upon annealing at 320◦C, the system segregates into stoichiometric FeGe regions with long-range CDW order and regions with stacking faults that act as nucleation sites for the CDW. The presence or absence of CDW order greatly affects the anomalous Hall effect, incommensurate magnetic order, and spin-lattice coupling in FeGe, thus placing FeGe as the only known kagome lattice material with a tunable CDW and magnetic order potentially useful for sensing and information transmission.

References:

[1] Nature 609, 490 (2022); Nature Physics 19, 814 (2023); Nature Communications 14, 6183 (2023); Nature Communications 15, 1918 (2024); Phys. Rev. Lett. 133, 046502 (2024).

[2] Phys. Rev. Lett. 132, 256501 (2024); Nature Communications 15, 6262 (2024).

Abstract: Synapses represent a fundamental unit of information transfer during cognition. They accomplish this by a process called presynaptic vesicle exocytosis, which can occur either spontaneously or by stimulation (called evoked release). It has been well established that evoked release is probabilistic in nature, but it has been less clear what mechanisms mediate spontaneous exocytosis. Understanding spontaneous exocytosis is important because it is an essential maintenance mechanism for synaptic connections and memory formation. In this talk I will introduce the complex set of biological parameters and fundamental molecular mechanics of how synapses communicate in a probabilistic manner. I will then present our recent theoretical and experimental work developing a conceptual framework, based on entropic force, that shows how presynapses regulate spontaneous exocytosis using the same complex set of biological parameters. I will discuss how this spontaneous exocytosis process is regulated during what is called synaptic plasticity, which is a fundamental mechanism of memory formation

Forces at biological interfaces: insights from neutron reflectometry

Abstract:  

Neutron reflectometry (NR) is a powerful method to interrogate the structure of multilayered thin films at interfaces and has found application in many areas in both hard and soft condensed matter. A key feature of neutron scattering for structural analysis is its isotope sensitivity, which can often be exploited in biological or biomimetic systems just by changing the aqueous medium from H2O to D2O. In many cases, NR is the most direct structural technique to probe interfacial phenomena in these systems; in this talk, following a general introduction to NR, I will discuss two examples in which NR revealed the strong interfacial forces at the surface of lipid bilayer membranes. In the first, https://pubs.acs.org/doi/full/10.1021/jacs.3c12348, counterintuitively, charged membranes are observed to strongly repel neutral nanoscale particles from its surface, creating a water-rich exclusion zone near the membrane surface. I will show that this effect is related to the formation of extremely strong local field gradients in the electric double layer; these repel neutral particles both by dielectrophoresis and counterion pressure. In the second, https://doi.org/10.1021/acs.langmuir.1c00214, I will demonstrate how substrate-supported lipid bilayers can be decoupled from the substrate by tuning the surface charge of the substrate. Finally, I will discuss several recent developments in NR: the new, highly parallelized CANDOR reflectometer at the NIST Center for Neutron Research, and the role that automation and active learning can play in accelerating data collection and discovery with NR.