Results 91 to 100 of about 77,334 (248)
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 +6 more
wiley +1 more source
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 +5 more
wiley +1 more source
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
Decompositions of Complete Multigraphs into Cyclic Designs
JOIV: International Journal on Informatics Visualization, 2020 Let and be positive integer, denote a complete multigraph. A decomposition of a graph is a set of subgraphs of whose edge sets partition the edge set of .
Mowafaq Alqadri +2 more
doaj +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 +7 more
wiley +1 more source
Lower Bound on the Number of Hamiltonian Cycles of Generalized Petersen Graphs
Discussiones Mathematicae Graph Theory, 2020 In this paper, we investigate the number of Hamiltonian cycles of a generalized Petersen graph P (N, k) and prove that Ψ(P(N,3))⩾N⋅αN,\Psi ( {P ( {N,3} )} ) \ge N \cdot {\alpha _N}, where Ψ(P(N, 3)) is the number of Hamiltonian cycles of P(N, 3) and αN ...
Lu Weihua, Yang Chao, Ren Han
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy +8 more
wiley +1 more source
X‐ray magnetic circular dichroism
Major Reference Works, Page 508-515., 2022 International Tables for Crystallography is the definitive resource and reference work for crystallography and structural science.
Each of the eight volumes in the series contains articles and tables of data relevant to crystallographic research and to applications of crystallographic methods in all sciences concerned with the ...
Gerrit van der Laan C. Chantler +2 more
wiley +1 more source
Arc-Disjoint Hamiltonian Cycles in Round Decomposable Locally Semicomplete Digraphs
Discussiones Mathematicae Graph Theory, 2018 Let D = (V,A) be a digraph; if there is at least one arc between every pair of distinct vertices of D, then D is a semicomplete digraph. A digraph D is locally semicomplete if for every vertex x, the out-neighbours of x induce a semicomplete digraph and ...
Li Ruijuan, Han Tingting
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source