Results 101 to 110 of about 77,685 (270)

On the existence of Hamiltonian cycles in hypercubes [PDF]

Notes on Number Theory and Discrete Mathematics
Building on the results of our previous work on Euclidean leaper tours, considering all integers k>1 and h>0, we study the existence of Hamiltonian cycles in the vertex set C(2,k):={0,1}ᵏ of the k-dimensional hypercube when the Euclidean distance between
Gabriele Di Pietro, Marco Ripà
doaj   +1 more source

The S-Hamiltonian Cycle Problem

CoRR
Determining if an input undirected graph is Hamiltonian, i.e., if it has a cycle that visits every vertex exactly once, is one of the most famous NP-complete problems. We consider the following generalization of Hamiltonian cycles: for a fixed set $S$ of natural numbers, we want to visit each vertex of a graph $G$ exactly once and ensure that any two ...
Antoine Amarilli, Arthur Lombardo, Mikaël Monet   +2 more
openaire   +2 more sources

QAOA on Hamiltonian Cycle problem

CoRR, 2023
I use QAOA to solve the Hamiltonian Circle problem. First, inspired by Lucas, I define the QUBO form of Hamiltonian Cycle and transform it to a quantum circuit by embedding the problem of $n$ vertices to an encoding of $(n-1)^2$ qubits. Then, I calcluate the spectrum of the cost hamiltonian for both triangle case and square case and justify my ...
openaire   +2 more sources

Discovery of an Ideal HgO12 Icosahedron and Magnetodielectric Coupling in HgCu3Ti4O12

Advanced Electronic Materials, EarlyView.
HgCu3Ti4O12, an A‐site‐ordered quadruple perovskite with a perfectly regular HgO12 coordination, has been synthesized for the first time using high‐pressure methods. The material shows antiferromagnetic ordering at 31 K together with an intrinsic dielectric anomaly, implying possible magnetodielectric coupling.
Haoyu Zheng, Jianfa Zhao, Jun Deng, Lei Duan, Xudong Shen, Zhiwei Hu, Luchuan Shi, Yuling Dai, Xiaoming Chen, Xubin Ye, Jun Zhang, Wenmin Li, Zheng Deng, Xiancheng Wang, Chang‐Yang Kuo, Chien‐Te Chen, Youwen Long, Runze Yu, Changjiang Xiao, Changqing Jin   +19 more
wiley   +1 more source

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

Advanced 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, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

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

Advanced 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, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

The Hamiltonian and Hypohamiltonian of Generalized Petersen Graph (GP_(n,9))

Jambura Journal of Mathematics
The study of Hamiltonian and Hypohamiltonian properties in the generalized Petersen graph GP_{n,k} is interesting due to the unique structure and characteristics of these graphs. The method employed in this study involves searching for Hamiltonian cycles
Susilawati Susilawati, Iis Nasfianti, Efni Agustiarini, Dinda Khairani Nasution   +3 more
doaj   +1 more source

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

Advanced 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, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source