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This Perspective explores attosecond physics in solids, focussing on high harmonic generation and attosecond transient absorption and reflection spectroscopy. Combining physical realizations and theoretical concepts is a challenge for future progress.
van der Waals polytypes are a class of periodic crystals that differ in their stacking configurations and can transform from one to another by discrete interlayer shifts. This Perspective discusses recent reports of the properties, structural stabilities and switching responses of polytypes and highlights challenges towards multiferroic opportunities.
This Perspective surveys the role of thermal nonlinearity in figures of merit through a multiscale physical understanding to advance heat harvesting technologies beyond linear processes, focusing on ‘nonlinear heat harvesting’, which potentially contributes to sustainable energy transition and decarbonization goals.
Attosecond science is a versatile discipline for studying ultrafast dynamics in matter on the microscopic scale. This Perspective explores the theoretical and experimental developments in this field focusing on distinguishing genuinely quantum observations from classical phenomena.
The dynamics of water freezing and ice melting in natural environments involves many intricate fluid mechanics processes. To tackle these complexities, examining them in well-controlled laboratory settings proves highly advantageous.
This Perspective explores the potential of using tailored fields to investigate chiral matter and ultrafast chiral dynamics. Light fields with well-defined symmetry properties can open new opportunities for research in chiral light–matter interactions.
CERN marks this year a major anniversary, of 70 years at the forefront of accelerator technology for high-energy physics. The story of its accelerator complex and 27-km tunnel — a major achievement in engineering and physics — is still unfolding.
The Rydberg atomic array is an emerging quantum many-body physics platform, exhibiting rich physical phenomena, such as quantum spin liquids and quantum scar states. This Perspective analyses the latest progress in this system through a unified theoretical framework — lattice gauge theory — providing new insights for quantum simulation.
This Perspective examines the pivotal role physicists have in the development and advancement of high-performance computing from its inception to the exascale era, highlighting key contributions and future challenges.
This Perspective summarizes the recent progress of topological physics in thermal metamaterials and thus proposes a new research field, ‘topological thermotics’, which is inspired by topological photonics and topological acoustics in wave metamaterials.
Advances in artificial-intelligence-assisted mathematical investigations suggest that human–machine collaboration will be an integral part of future theoretical research.
This Perspective explores how the physical properties of these topological nanoscale systems, such as magnetic skyrmions and ferroelectric domain walls, can be leveraged for reservoir computing.
This Perspective argues that the development of 99Mo production methods complementary to reactor-based methodology is strategic in the short-to-medium term. Localized and resilient 99Mo production routes might guarantee access to important diagnostic procedures even in the case of unpredictable global events.
Quantum sensing exploits properties of quantum systems to go beyond what is possible with traditional measurement techniques, hence opening exciting opportunities in both low-energy and high-energy particle physics experiments.
Machine learning techniques may appear ill-suited for application in fields that prioritize rigor and deep understanding; however, they have recently found unexpected uses in theoretical physics and pure mathematics. In this Perspective, Gukov, Halverson and Ruehle have discussed rigorous applications of machine learning to theoretical physics and pure mathematics.
Neural operators learn mappings between functions on continuous domains, such as spatiotemporal processes and partial differential equations, offering a fast, data-driven surrogate model solution for otherwise intractable numerical simulations of complex real-world problems.
Generative machine learning models seek to approximate and then sample the probability distribution of the data sets on which they are trained. This Perspective article connects these methods to historical studies of information processing and attractor geometry in nonlinear systems.
Understanding the W boson as accurately as possible, including knowing its mass, has been a priority in particle physics for decades. This Perspective article gives an overview of the role of the W boson mass in the Standard Model and its extensions and compares techniques for measuring it.
