Results 91 to 100 of about 3,188,937 (280)

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

On Hamiltonian cycles in hypergraphs with dense link graphs [PDF]

J. Comb. Theory B, 2020
J. Polcyn, Christian Reiher, V. Rödl, B. Schülke   +3 more
semanticscholar   +1 more source

Atomically Dispersed Transition Metals on Holey Graphyne as an Efficient Electrocatalyst for Nitrogen Reduction Reactions

Advanced Energy and Sustainability Research, EarlyView.
Transition metal‐decorated holey graphyne (TM@hGY) catalysts are evaluated for electrochemical nitrogen reduction. Cr@hGY demonstrates exceptional catalytic activity with a limiting potential of −0.34 V, exhibiting high selectivity for ammonia synthesis and low hydrogen evolution, offering a promising strategy for efficient, sustainable ammonia ...
Mihir Ranjan Sahoo, Bilal Aladerah, Avijeet Ray, Nirpendra Singh   +3 more
wiley   +1 more source

Arc-disjoint hamiltonian paths in Cartesian products of directed cycles [PDF]

, 2022
Iren Darijani, Babak Miraftab, Dave Witte Morris   +2 more
openalex   +1 more source

Designing Hamiltonian Cycles

, 2013
Historically, the minimal length Hamiltonian cycles in a random point cloud lying inside a given rectangle are computed by partitioning this rectangle. We have used successive convex hulls of the set of points, in order to obtain partitions better suited for this purpose.
de Arriba Perez, Francisco, Corbacho Rosas, Eusebio Tirso, Vidal Vázquez, Ricardo   +2 more
openaire   +1 more source

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +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

On Hamiltonian Paths and Cycles in Sufficiently Large Distance Graphs [PDF]

, 2014
Christian Löwenstein, Dieter Rautenbach, Roman Soták   +2 more
openalex   +1 more source

Hamiltonian Cycle in Folded Hypercubes With Highly Conditional Edge Faults [PDF]

, 2020
Che-Nan Kuo, Yu‐Huei Cheng
openalex   +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