Results 101 to 110 of about 382 (214)

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

Iterative Synthesis of Pyrene–Coronene Molecular Graphene Nanoribbons

Angewandte Chemie, EarlyView.
An iterative approach delivers a new family of undoped, cove‐edged graphene nanoribbons with lengths up to 9.1 nm that exhibit both molar absorptivity and fluorescence brightness values on the order of 105 M−1 cm−1 and an intrinsic charge‐carrier mobility of 475 ± 32 cm2 V−1 s−1.
Miguel A. Medel, Guanzhao Wen, Adrián Morón‐Blanco, Mischa Bonn, Hai I. Wang, Manuel Melle‐Franco, Aurelio Mateo‐Alonso   +6 more
wiley   +2 more sources

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

Energy Conditions for Hamiltonicity of Graphs

Discrete Dynamics in Nature and Society, 2014
Let G be an undirected simple graph of order n. Let A(G) be the adjacency matrix of G, and let μ1(G)≤μ2(G)≤⋯≤μn(G) be its eigenvalues. The energy of G is defined as ℰ(G)=∑i=1n‍|μi(G)|. Denote by GBPT a bipartite graph.
Guidong Yu, Gaixiang Cai, Miaolin Ye, Jinde Cao   +3 more
doaj   +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

On pre-Hamiltonian Cycles in Hamiltonian Digraphs

, 2014
Let $D$ be a strongly connected directed graph of order $n\geq 4$. In \cite{[14]} (J. of Graph Theory, Vol.16, No. 5, 51-59, 1992) Y. Manoussakis proved the following theorem: Suppose that $D$ satisfies the following condition for every triple $x,y,z$ of vertices such that $x$ and $y$ are non-adjacent: If there is no arc from $x$ to $z$, then $d(x)+d(y)
openaire   +2 more sources

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

On edge-graceful labeling and deficiency for regular graphs

AKCE International Journal of Graphs and Combinatorics, 2018
An edge-graceful labeling of a finite simple graph with vertices and edges is a bijection from the set of edges to the set of integers such that the vertex sums are pairwise distinct modulo , where the vertex sum at a vertex is the sum of labels of all ...
Tao-Ming Wang, Guang-Hui Zhang
doaj   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

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, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

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