Results 101 to 110 of about 1,982,910 (295)

Sticker Valence Governs Chain Collapse and Liquid‐Liquid Phase Separation in Peptide‐Inspired Associative Polymers

open access: yesAdvanced Science, EarlyView.
Peptide‐inspired associative polymers with arginine‐like guanidinium stickers and lysine‐like ammonium spacers undergo chain collapse and liquid‐liquid phase separation induced by reversible sticker associations. Sticker valence and tunable association strength jointly control the phase behavior, chain conformation, and interfacial cohesion of these ...
Seung‐Hwan Oh   +7 more
wiley   +1 more source

Local properties of graphs that induce global cycle properties [PDF]

open access: yesOpuscula Mathematica
A graph \(G\) is locally Hamiltonian if \(G[N(v)]\) is Hamiltonian for every vertex \(v\in V(G)\). In this note, we prove that every locally Hamiltonian graph with maximum degree at least \(|V(G)| - 7\) is weakly pancyclic.
Yanyan Wang, Xiaojing Yang
doaj   +1 more source

Van der Waals Heterostructures for Next‐Generation Spintronics: Multiferroic‐Mediated Magnetoelectric Properties

open access: yesAdvanced Science, EarlyView.
As CMOS technology approaches fundamental energy limits, new paradigms for low‐power information processing are required. Magnetic systems are attractive for their spin transport, yet current‐induced magnetization control is energy inefficient. Multiferroic heterostructures enable energy‐efficient electric field manipulation of magnetism.
Donghyeon Lee   +3 more
wiley   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

open access: yesAdvanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu   +6 more
wiley   +1 more source

Integrating Hamiltonian systems defined on the Lie groups SO(4) and SO(1,3) [PDF]

open access: yes, 2007
In this paper we study constrained optimal control problems on semi-simple Lie groups. These constrained optimal control problems include Riemannian, sub-Riemannian, elastic and mechanical problems. We begin by lifting these problems, through the Maximum
Biggs, James, Holderbaum, William
core   +2 more sources

Passivity Enforcement via Perturbation of Hamiltonian Matrices [PDF]

open access: yes, 2004
This paper presents a new technique for the passivity enforcement of linear time-invariant multiport systems in statespace form. This technique is based on a study of the spectral properties of related Hamiltonian matrices.
Grivet-Talocia, S.   +2 more
core   +2 more sources

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

Hamiltonian Strongly Regular Graphs [PDF]

open access: yes
We give a sufficient condition for a distance-regular graph to be Hamiltonian. In particular, the Petersen graph is the only connected non-Hamiltonian strongly regular graph on fewer than 99 vertices.Distance-regular graphs;Hamilton cycles JEL ...
Brouwer, A.E., Haemers, W.H.
core  

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen   +5 more
wiley   +1 more source

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