Results 211 to 220 of about 197,846 (331)

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

Enhancing Neuromorphic Robustness via Recurrence Resonance: The Role of Shared Weak Attractors in Quantum Logic Networks. [PDF]

Biomimetics (Basel)
Huang Y, Gunji YP.
europepmc   +1 more source

Quantum correlations in systems of indistinguishable particles/ topological states of matter in a number conserving setting

, 2015
Fernando Iemini de Rezende Aguiar
openalex   +1 more source

Extreme Transverse Magnetoresistance in TiZn16

Advanced Electronic Materials, EarlyView.
Extreme magnetoresistance is observed in TiZn16, despite its metallic nature and a highly complex electronic structure with multiple electron and hole Fermi surfaces. High‐magnetic‐field measurements further revealed the presence of a cylindrical open orbit, which may help resolve the origin of the extreme magnetoresistance.
Aaron Chan, Guoxin Zheng, Dechen Zhang, Yuan Zhu, Kaila Jenkins, Kuan‐Wen Chen, Haozhe Wang, Weiwei Xie, Brianna Billingsley, Tai Kong, Na Hyun Jo, Lu Li   +11 more
wiley   +1 more source

Topological Phase Transition in Two-Dimensional Magnetic Material CrI₃ Bilayer Intercalated with Mo. [PDF]

Materials (Basel)
Yin CE, Huang A, Jeng HT.
europepmc   +1 more source

Advances in Thermoelectric Thin Films Grown by Atomic Layer Deposition: A Critical Review of Performance and Challenges

Advanced Energy Materials, EarlyView.
This review highlights the use of atomic layer deposition (ALD) for fabricating thermoelectric thin films with atomic‐scale control. Four material classes—chalcogenides, doped oxides, ternary oxides, and multilayered structures—are compared in terms of growth dynamics, structure–property relationships, and thermoelectric performance. The precise tuning
Jorge Luis Vazquez‐Arce, Dong‐Ho Shin, Shuyue Yao, Shiyang He, Kornelius Nielsch, Amin Bahrami   +5 more
wiley   +1 more source

New magnetic topological materials from high-throughput search. [PDF]

Sci Adv
Robredo I, Xu Y, Jiang Y, Felser C, Bernevig BA, Elcoro L, Regnault N, Vergniory MG.   +7 more
europepmc   +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

Quantum neural network-based compensation of distorted orbital angular momentum beams in complex media. [PDF]

Sci Rep
Manavalan G, Arnon S.
europepmc   +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