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Explore all talks and presentations from APS Physics. Dive deep into the latest insights, research, and trends from the world's leading experts.
Exploring nuclear threat reduction strategies and the role of physicists in advocating for disarmament, emphasizing the urgency of addressing current global nuclear risks.
Explore US plans for new nuclear weapons, including rationale, design validation, and potential consequences of deploying a replacement warhead for silo-based missiles.
Explore atomic-scale heterostructures and emerging physics in 2D quantum materials. Learn about stacking techniques, high-mobility graphene, and novel phenomena like magic angles and quasicrystals.
Exploring intracellular phase transitions and their role in organizing cellular contents, with focus on liquid-liquid phase separation and new approaches for spatiotemporal control.
Explores physical vapor deposition for creating ultra-stable glasses with controlled molecular packing, discussing dense structures, anisotropic ordering, and applications in organic electronics.
Explore federal policy making through examples like microelectronics and quantum technology. Gain insights on stakeholder dynamics, S&T's role in addressing national needs, and effective strategies for influencing policy decisions.
Learn effective strategies for communicating scientific advice to policymakers from a scientist-legislator. Gain insights on maximizing impact, understanding policy complexities, and engaging in the political process.
Exploring verification challenges and technologies for North Korea's denuclearization, addressing security concerns while ensuring effective monitoring of residual nuclear arsenals.
Explore the intersection of machine learning and renormalization group theory, focusing on real-space applications and information theory perspectives in physics.
Explore machine learning advancements in molecular dynamics simulations, covering symmetries, training methods, and applications in hydrocarbon reactions and IR spectra analysis.
Explore the fusion of physics and AI algorithms, covering neural programming, hierarchies, equations, and applications in autonomous systems and technology.
Explore multi-scale entanglement renormalization and neural network renormalization group, connecting deep learning with quantum physics concepts for advanced computational techniques.
Explore generative models for quantum state learning, covering neural networks, RBMs, and applications to GHZ states and local Hamiltonians.
Explore machine learning techniques for predicting molecular energies from electron densities, with applications in reactive molecular dynamics and density functional theory.
Explore deep learning applications in neutrino physics, from classifying interactions to analyzing events, with insights on future developments in particle detection.
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