Multimessenger astronomy has entered an exciting new era with the recent discovery of both gravitational waves and cosmic neutrinos.  I will focus on extremely energetic neutrinos as particles that can uniquely probe the most extreme astrophysics sources at cosmic distances, as well as fundamental physics in an unexplored energy regime.  While the optical detection technique remains the most powerful for neutrino detection over a broad energy range, radio techniques have emerged in the last two decades as the most promising for a long-term program to push the neutrino frontier by over a factor of 1000 in energy.   I will present the latest results from the field of high energy neutrino astrophysics, with a focus on the balloon-borne ANITA experiment and the in-ice South Pole array ARA.  I will also give an overview of the many exciting projects in this field that are on the horizon, and their anticipated impact in terms of the astrophysics and particle physics questions that we seek to answer.

The accelerated discovery and design of new quantum materials requires atomic-level information about chemical reactions, phase transformations, mechanical deformations and other collective and emergent quantum phenomena. Several techniques have been developed recently that can learn the potential energy surface (PES) of complex materials. Machine Learning (ML) models, particularly deep neural networks, have proven capable of learning highly complex non-linear relationships between atomic structure and properties and theory and experiments. In this talk, I will describe two examples of ML-driven MD called neural-network quantum molecular dynamics (NNQMD) to tackle problems related to large systems and long trajectories that cannot be investigated by Quantum Molecular Dynamics (QMD).

First, we use NNQMD for quantitatively characterizing the intermediate range order, manifested as first sharp diffraction peak in GeSe₂. In the second example, we compute the dielectric constant, ε₀, and its temperature dependence for liquid water using fluctuations in macroscopic polarization using two coupled neural network models. The first network, NNQMD, learns the PES of liquid water from QMD training data. The second network, neural-network maximally localized Wannier functions, NNMLWF, is trained to predict dipole moments.

I will also briefly discuss applications of ML to discovery of new dielectric polymer materials with high breakdown strengths and to optimization of chemical vapor deposition synthesis of quantum materials.

Precision rotation sensing is useful for navigation, geophysics, and tests of fundamental physics. Atom interferometers provide, by some measures, the most sensitive method for rotation sensing achieved to date. However, the best performance requires freely falling atoms in a large experimental apparatus. Many applications, such as navigating a vehicle, will benefit from a more compact geometry. One method to achieve this is by using trapped atoms that are suspended against gravity. We have implemented such an interferometer and used it to measure a rotation rate comparable to that of the Earth. The most recent iteration of the interferometer has demonstrated improvements by a factor of ten in rotation sensitivity and trap stability. A second new apparatus reduces the scale of the vacuum chamber and optical system to roughly the size of a microwave oven.

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Atom interferometers exploit spatially delocalized quantum states to make a wide variety of highly precise measurements.  Recent technological advances have opened a path for atom interferometers to contribute to multiple areas at the forefront of modern physics, including searches for wave-like dark matter, gravitational wave detection, and fundamental quantum science.  In this colloquium, I will describe MAGIS-100, a 100-meter-tall atom interferometer being built at Fermilab to pursue these directions.  MAGIS-100 will serve as a prototype gravitational wave detector in a new frequency range, between the peak sensitivities of LIGO and LISA, that is promising for pursuing cosmological signals from the early universe and for studying a broad range of astrophysical sources.  In addition, MAGIS-100 will search for wave-like dark matter, probe quantum mechanics in a new regime in which massive particles are delocalized over macroscopic scales in distance and time, and act as a testbed for advanced quantum sensing techniques.  Finally, I will discuss the potential and motivation for follow-on atomic detectors with even longer baselines.

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