National Science Review最新文献

The integration of machine learning into fundamental science has opened new avenues for addressing long-standing challenges rooted in mathematical limitations. For instance, while topological invariants are essential for characterizing topological phases of matter, no single invariant is universally applicable. This limitation explains why, over decades of classifying topological phases-primarily in Hermitian systems-many phases initially deemed 'trivial' were later recognized as topological. Recently, the discovery of non-Hermitian band topology has driven substantial efforts in non-Hermitian topological classification, leading to the development of new topological invariants. However, these invariants still fail to capture all non-Hermitian topological features. Here, without relying on any topological invariant, we develop a machine-learning algorithm for the unsupervised classification of symmetry-protected non-Hermitian topological phases. By utilizing random Hamiltonians, we unsupervisedly construct a topological periodic table without requiring advanced mathematical knowledge. Furthermore, based on the learning results, we derive a formula that reveals the impact of parity transformation on periodicity. Our algorithm can also account for boundary effects, enabling the exploration of open-boundary influences on the topological phase diagram. These findings establish an unsupervised approach for classifying symmetry-protected non-Hermitian topological phases, uncover previously unnoticed topological features in non-Hermitian systems, and provide valuable guidance for both theoretical advancements and experimental realizations.

This review summarizes recent advances in the origin of turbulence. The soliton-like coherent structure (SCS) has been identified not only as a common structure in natural and K-type transitions, but also as a fundamental structure in N-type, O-type and bypass-type transitions, which dominate turbulence generation. It has also been confirmed as an essential structure in turbulent boundary layers. Notably, the SCS has been successively observed in pipe flows, stratified flows, mixing layers, jet flows, falling films and wakes. These findings collectively indicate that the SCS serves as a fundamental structure in shear flows, providing significant insights into the origin of shear turbulence.

Ferroelectric-based artificial synapses have emerged as fascinating candidates for the development of intelligent sensor-memory-computing (SMC) systems, thanks to the remarkable nonvolatile properties and abundant polarization states that ferroelectrics offer. However, simultaneously modulating the ferroelectric synapse through optical and electrical excitation is challenging. Herein, we propose a high-throughput strategy for designing optoelectronic co-modulated ferroelectric synapses. This strategy involves designing a ferroelectric field-effect transistor (FeFET) based on the Pb(Zr_0.2Ti_0.8)O₃/InGaZnO (IGZO) heterostructure, which includes an IGZO homostructure, followed by high-throughput screening of IGZO materials that enable both optical and electrical modulation. The transistors with optoelectronic co-modulated synaptic functionalities are subsequently screened from a set of high-throughput FeFETs. Based on these optoelectronic co-modulated ferroelectric synapses, an artificial SMC system that can simultaneously sense and recognize images is constructed, achieving a high recognition accuracy of 88.42% and this SMC system simultaneously exhibits the advantages of reduced hardware overheads, fast speed and low power consumption. Our work introduces a novel strategy for designing multifunctional artificial synapses from materials to devices, which may represent a new paradigm in the development of high-performance SMC systems.

Shortwave infrared (SWIR) photodetectors are essential for industrial, medical and security applications, but their adoption is limited by reliance on costly epitaxial semiconductors. Solution-processed organic and colloidal quantum dot (CQD) semiconductors offer a promising alternative, although their devices often suffer from high dark-current under reverse bias. We demonstrate an approach to precise work function modulation in SWIR photodetectors using conjugated polyelectrolytes with mixed counterions. This strategy achieves continuous work function tuning and interfacial defect passivation, significantly reducing dark current in solution-processed photodetectors. The optimized CQD photodetectors exhibit a dark-current density two orders of magnitude lower than that of conventional devices at -0.1 V bias (6.7 × 10^-8 A cm^-2), achieving a linear dynamic range of 130 dB, specific detectivity of 4.3 × 10¹² Jones, and cutoff frequency of 107 kHz under 1550 nm illumination. Integrated with a complementary metal-oxide-semiconductor read-out integrated circuit, the resulting SWIR imager demonstrates high-resolution performance (1280 × 1024 pixels), providing a viable alternative to InGaAs-based imagers.

Heavy investments, novel institutions and intra-regional ties are fueling the scientific rise of the Guangdong-Hong Kong-Macao Greater Bay Area.

The crystal-symmetry-paired spin-momentum locking (CSML), arising from the intrinsic crystal symmetry that connects different magnetic sublattices in altermagnets, enables many exotic spintronics properties, such as unconventional piezomagnetism and non-collinear spin currents. However, the shortage of monolayer altermagnets restricts further exploration of dimensionally confined phenomena and applications of nanostructured devices. Here, we propose general chemical design principles inspired by sublattice symmetry of the layered altermagnet V[Formula: see text](Se,Te)[Formula: see text]O through symmetry-preserving structural modification and valence-adaptive chemical substitutions. In total, we construct 2600 candidates across four structural frameworks, M[Formula: see text]A[Formula: see text]B[Formula: see text] and their Janus derivatives. High-throughput calculations identify 612 potential altermagnets with Néel-ordered ground states, among which 79 exhibit CSML Dirac cones that enable spin-polarized ultra-fast transport. These materials also feature different ground-state magnetic orderings and demonstrate diverse electronic behaviors, ranging from semiconductors and metals to half-metals and Dirac semimetals. This work not only reveals abundant monolayer altermagnets, but also establishes a rational principle for their design, opening the gates to the exploration of confined magnetism and spintronics in atomically thin systems.

Simultaneously attaining high energy density and long cycling life remains a critical challenge for aluminum-organic batteries (AOBs) due to low operating voltage, limited active sites and unstable coordination structure of organic cathodes. Herein, we design a multisite super-crosslinked sulfur-heterocyclic polymer cathode. The electronegative sulfur heterocycles can significantly weaken the electron-donating effect, promoting the operating voltage to 2.0 V (average ∼1.7 V), which is a breakthrough for AOBs (<1.5 V for almost all AOBs). Tailoring the linking patterns of polymers to increase active sites can maximize redox activity to 12-electron-transfer, contributing to a high capacity of 150 mAh g^-1. The designed organic cathode achieves 255 Wh kg^-1 energy density, breaking the upper limit of conventional graphite cathodes (∼200 Wh kg^-1). Notably, the weak coordination interaction between C‒S⁺‒C radicals and AlCl₄ ^- carriers ensures structural stability, enabling the battery's excellent low-temperature durability, with almost 100% capacity retention after 12 000 cycles at -20°C.