Results 241 to 250 of about 15,325 (297)

Voltage-controlled topological spin textures in the monolayer limit. [PDF]

Nat Commun
Wu Y, Peng B, Zeng Z, Yang C, Lu H, Zhou P, Xie J, Liang D, Zhang L, Yan P, Guo H, Che R, Deng L.   +12 more
europepmc   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Descent from a common ancestor restricts exploration of protein sequence space. [PDF]

Proc Natl Acad Sci U S A
Isakova LH, Streltsova E, Bochkareva OO, Vlasov PK, Kondrashov FA.   +4 more
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Chiral Nonlinear Enhancement with Opposite Circular Dichroism Empowered by Dual Bound States in the Continuum. [PDF]

Materials (Basel)
Liu X, Wang L, Meng H.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Symmetry-driven spin splitting in altermagnets: an angle-resolved photoemission spectroscopy perspective. [PDF]

Nano Converg
Liu J, Ma X, Zhang X, Jing W, Liu Z, Shen D.   +5 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Structured coherent thermal emission from non-Hermitian metasurfaces. [PDF]

Nat Commun
Sun K, Wang K, Li W, Cai Y, Wang W, Kivshar Y, Han Z.   +6 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source