Results 61 to 70 of about 634 (124)

Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi‐Variable Systems

Advanced Science, EarlyView.
UHSR translates complex chemical behavior into clear and explainable equations. Applied to thin‐layer chromatography, it automatically uncovers the mathematical rules linking a molecule's structure to its polarity. This approach matches the accuracy of advanced AI while providing interpretable results, earning greater trust from chemists. The method is
Siyu Lou, Chengchun Liu, Dongxiao Zhang, Yuntian Chen, Fanyang Mo   +4 more
wiley   +1 more source

A TtAgo‐Driven Autocatalytic Circuit with Thermal‐Enhanced Kinetics for One‐Pot Nucleic Acid Detection

Advanced Science, EarlyView.
This study proposes a universal catalytic DNA circuits termed TACTIC (Thermus thermophilus protein‐driven autocatalytic circuit) for one‐pot detection of DNA and RNA in multiple clinical samples. Integrated with machine learning, TACTIC accurately profile the distinct expression of four extracellular vesicle‐derived miRNAs across different samples and ...
Zuowei Xie, Ruijia Deng, Ben Niu, Yingjie Yang, Shuang Zhao, Hongzhao Yang, Meilin Gong, Jie Luo, Yu Tang, Jing Sheng, Yan Pi, Ming Chen, Kai Chang   +12 more
wiley   +1 more source

Room‐Temperature Magnetic Skyrmions and Intrinsic Anomalous Hall Effect in a Nodal‐Line Kagomé Ferromagnet MnRhP

Advanced Science, EarlyView.
MnRhP has been identified as a novel kagomé magnet that hosts magnetic skyrmions above room temperature and exhibits a large intrinsic anomalous Hall effect. The latter originates from Berry curvature associated with gapped nodal lines, establishing MnRhP as a promising platform for exploring high‐temperature topological phenomena in both real and ...
Kosuke Karube, Ming‐Chun Jiang, Lukas Keller, Jonathan S. White, Yi Ling Chiew, Xiuzhen Yu, Guang Yu Guo, Ryotaro Arita, Yoshinori Tokura, Yasujiro Taguchi   +9 more
wiley   +1 more source

Some new generalized difference spaces of nonabsolute type derived from the spaces ℓ p and ℓ ∞. [PDF]

ScientificWorldJournal, 2013
Başar F, Karaisa A.
europepmc   +1 more source

Emerging of Anomalous Higher‐Order Topological Phases in Altermagnet/Topological Insulator Heterostructure by Floquet Engineering

Advanced Science, EarlyView.
We combine altermagnet with topological insulators and subject the structure to Floquet driving. This breaks time‐reversal symmetry and creates a new type of higher‐order topological insulator. Its key feature is the emergence of programmable “0/π‐corner modes” that can be controlled by magnetic field direction, offering a novel dynamic platform for ...
Donghao Wang, Arnob Kumar Ghosh, Yongchun Tao, Fusheng Ma, Cheng Song   +4 more
wiley   +1 more source

Realization of a Bilayer Elastic Topological Insulator

Advanced Science, EarlyView.
Bilayer elastic wave topological insulators are experimentally realized, introducing the layer degree of freedom to access four topological phases. This enables diverse domain walls and transmission behaviors, including interlayer conversion and beam splitting.
Chengzhi Ma, Zhiwei Song, Zheyu Cheng, Jiu Hui Wu, Baile Zhang   +4 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

A General and Efficient Framework for the Rapid Design of Miniaturized, Wideband, and High‐Bit RIS

Advanced Electronic Materials, EarlyView.
A general and efficient framework is proposed for the rapid design of high‐performance reconfigurable intelligent surfaces (RISs). This framework integrates advanced antenna design techniques and incorporates various load types, quantities, and values to achieve the design of high‐performance RISs.
Jun Wei Zhang, Zheng Xing Wang, Wanwan Cao, Zhen Jie Qi, Li Jie Wu, Han Qing Yang, Qun Yan Zhou, Si Ran Wang, Hui Dong Li, Jun Yan Dai, Jiang Luo, Jun Wei Wu, Jia Nan Zhang, Zhen Zhang, Qiang Cheng   +14 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source