Superconductivity in Layered Nickelates

Unconventional superconductivity in proximity to various strongly correlated electronic phases has been a recurring theme in materials as diverse as heavy fermion compounds, cuprates, pnictides, and twisted bilayer graphene. Here we will introduce a new and growing family of layered nickelate superconductors. The initial discovery of superconductivity in infinite-layer nickelates was motivated by looking for an electronic analog of the cuprates. Notable aspects are a doping-dependent superconducting dome, strong magnetic fluctuations, and a landscape of unusual normal state properties from which superconductivity emerges. The subsequent discovery of superconductivity in bulk La 3 Ni 2 O 7  under high pressure is quite intriguing, in that the d-electron configuration is a priori quite different. Recently, we have used epitaxial strain in (La,Pr) 3 Ni 2 O 7  thin films to stabilize superconductivity at ambient pressure, which is promising to extend their experimental study and
development.

Bio: Harold Y. Hwang is Professor of Applied Physics (Stanford University) and Photon Science (SLAC National Accelerator Laboratory), Senior Fellow of the Precourt Institute for Energy, and Director of the Stanford Institute for Materials and Energy Sciences. He received a BS in Physics, BS and MS in Electrical Engineering from MIT (1993), and a PhD in Physics from Princeton University (1997). He was formerly a Member of Technical Staff at Bell Laboratories (1996-2003) and Professor at the University of Tokyo (2003-2010). His research is in condensed matter and materials physics, with a focus on correlated electrons and emergent phenomena in quantum materials, and heterostructures for energy applications and devices. He is a fellow of the American Physical Society, the American Academy of Arts and Sciences, and member of the National Academy of Sciences. Recognitions include the MRS Outstanding Young InvestigatorAward (2005), the IBM Japan Science Prize (Physics, 2008), the Ho-Am Prize (Science, 2013), the Europhysics Prize (2014), and the McGroddy Prize (2024).

Can Physics Stop Climate Change?

Can physics save the planet from climate change? No single solution will work, but this talk will explore how physks can help us fight climate change in the present and future. Physics helps us understand climate change. It also guides the design of energy sources that don't pollute the air as much as fossil fuels, like wind and solar power, and nuclear fusion. The physical science behind removing carbon dioxide already in the atmosphere gives us some hope that we can eventually fix the damage we've done. More radical schemes to cool the Earth will also be mentioned.

Brad Marston is a professor of physics at Brown University, and Director of the Brown Theoretical Physics Center. A graduate of Caltech, he received his Ph.D. from Princeton University in 1989 and did postdoctoral work at Cornell University as an IBM Fellow. He has been a visiting professor at MIT, a visiting associate at Caltech, a visiting professor at ENS-Lyon, and a General Member of the Kavli Institute for Theoretical Physics (KITP) at UC Santa Barbara. Marston is an Alfred P. Sloan fellow and a recipient of a National Young Investigator Award. In 2008 he was designated a NSF American Competitiveness and Innovation Fellow, and in 2010 an American Physical Society (APS) Outstanding Referee. Marston is a fellow and lifetime member of the American Physical Society (APS). He has chaired the Advisory Board of the KITP, and was a Councilor for the APS Division of Condensed Matter Physics (DCMP). He is the elected president of the APS for 2026.

Plasma Turbulence in Stellarators: Physics Insights from Machine Learning

The stellarator is a device for confining charged particles and plasma using geometrically optimized magnetic fields. It has applications both for fusion energy and for fundamental plasma physics. Turbulence in stellarator plasmas limits their temperature, and this turbulence depends strongly on the magnetic field geometry. To understand the nature of this dependence, we look for patterns in a new dataset of over 200,000 numerical turbulence simulations. We apply machine learning methods that respect physical invariances and are interpretable, uncovering analytic expressions of the geometry that correlate strongly with the turbulent heat flux. This example demonstrates one way that machine learning can go beyond black-box interpolation, to work in concert with traditional physics analysis.

Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study

Quantum computing is one of the most enticing emerging computational paradigms. It has the potential to revolutionize diverse areas within the future of computation. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, current processors are still too small and non-robust to be directly useful for any real-world applications today. Nevertheless, we are entering an era of unprecedented capabilities for the exploration of quantum algorithms and protocols beyond what is possible today. There is also the opportunity to map out large-scale architectures and estimate resources for early fault-tolerant quantum computing, tailored to specific applications, through codesign of algorithms, quantum error correction, and hardware. In this talk, I’ll discuss NASA’s work in assessing and advancing the potential of quantum computing, illustrating advances in algorithms, both near- and longer-term, in designing novel quantum error correction methods, in resource estimation, and in co-design. I’ll also highlight physics-inspired classical algorithms that can be used at the application scale today. The talk will conclude with a discussion of open research directions.

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Semiconductor quantum systems

Quantum technologies need photonics for scaling. This is true even for “non-photonic” quantum systems based on superconductors, or trapped atoms and ions in vacuum. For example, new types of spatial light modulators and switches are needed to trap and control atoms and ions, microwave to optical quantum transducers are needed for networking superconducting processors, chip-scale laser systems are required for controlling atoms or spin qubits in solids, and very high efficiency integrated photonics is needed for quantum networks, sensors, and chip-based semiconductor quantum systems. Unfortunately, these photonics functionalities and performances are not available even in today’s best integrated photonic systems. We show how inverse design (which combines AI hardware with new ty pes of physics solvers) can lead to much better photonics designs, and how new photonic materials combined with new nanofabrication and heterogenous integration can lead to desired performances. Specific examples include development of miniaturized titanium:sapphire lasers on chip, strontium titanate transducers, quantum network nodes in diamond, and a quantum simulator and computer with silicon carbide color centers.

The Illusion of "Self"-Driving Cars

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