Results 141 to 150 of about 377,401 (276)

Bond Softening and Elastic Confinement of Monolithic Dense Binary Alloys for High Volumetric Capacity of K‐Ion Battery Anodes

Angewandte Chemie, EarlyView.
Synergistically intrinsic‐extrinsic dual‐stabilization engineering to construct dense monolithic alloy architectures is proposed by integrating 2D binary alloy nanosheets into 3D elastic graphene networks. Such a dense monolithic anode achieves high volumetric and areal performance, enabling fast K+ transfer and superior structural recovery during ...
Yunyong Li, Yiru Zhou, Bingchun Wang, Xinying Wang, Lei Liu, Zhuhang Shao, Hao Wu, Zaowen Zhao, Yingqiang Wu, Xuerong Zheng, Xinwei Li, Wenwu Li, Yida Deng, Zaiping Guo, Ho Seok Park   +14 more
wiley   +2 more sources

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

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Predicting Crystal Structures and Ionic Conductivities in Li3 YCl6−x Brx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

Advanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

Geometry of the Kahan discretizations of planar quadratic Hamiltonian systems. [PDF]

Proc Math Phys Eng Sci, 2019
Petrera M, Smirin J, Suris YB.
europepmc   +1 more source

Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems. [PDF]

Sci Adv, 2019
Goto H, Tatsumura K, Dixon AR.
europepmc   +1 more source

Reducible problem for a class of almost-periodic non-linear Hamiltonian systems. [PDF]

J Inequal Appl, 2018
Afzal M, Ismaeel T, Jamal M.
europepmc   +1 more source

Gravitational Hamiltonian Systems and the Retarded Gravity Inequality. [PDF]

Entropy (Basel)
Yahalom A.
europepmc   +1 more source

Periodic Solutions of Asymptotically Linear Autonomous Hamiltonian Systems with Resonance. [PDF]

J Dyn Differ Equ, 2018
Gołȩbiewska A.
europepmc   +1 more source