Results 131 to 140 of about 267,752 (279)

Elucidating the Transition Kernel and Anharmonic Coupling in the Spin‐crossover Process of a [FeIII(qsal)2] CH3OSO3 Complex

Angewandte Chemie, EarlyView.
Ultrafast broadband transient absorption spectroscopy and multireference excited‐state nonadiabatic calculations in an open‐shell Fe(III) complex unveil the rich electronic and vibrational dynamics detailing the key reactive modes driving the spin‐crossover process.
Soumyajit Mitra, Dilara Farkhutdinova, Sebastian Mai, Stuart A. Hayes, Yifeng Jiang, Tadahiko Ishikawa, Kazuyuki Takahashi, Leticia González, R. J. Dwayne Miller   +8 more
wiley   +2 more sources

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

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

Existence of infinitely many periodic solutions for second-order nonautonomous Hamiltonian systems

Electronic Journal of Differential Equations, 2015
By using minimax methods and critical point theory, we obtain infinitely many periodic solutions for a second-order nonautonomous Hamiltonian systems, when the gradient of potential energy does not exceed linear growth.
Wen Guan, Da-Bin Wang
doaj  

Subharmonic solutions of first-order Hamiltonian systems

Advances in Nonlinear Analysis
The aim of this article is to study subharmonic solutions of superquadratic and asymptotically (constant) linear nonautonomous Hamiltonian systems in R2n{{\mathbb{R}}}^{2n} respectively, and to improve the results in Professor Liu’s [Subharmonic ...
Zhou Yuting
doaj   +1 more source

“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints

Advanced Intelligent Systems, EarlyView.
This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
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

Emergent Spin Hall Quantization and High‐Order van Hove singularities in Square‐Octagonal MA2Z4

Advanced Physics Research, EarlyView.
Square‐octagonal MA2Z4 (M = Mo/W, A = Si/Ge, Z = pnictogen) monolayers are predicted to realize quantum spin Hall insulators with nearly quantized spin Hall conductivity enabled by an emergent spin U(1) quasi‐symmetry. Materials with Z = As and Sb host quasi‐flat bands with high‐order van Hove singularities near the Fermi level, making them promising ...
Rahul Verma, Yash Vardhan, Hsin Lin, Bahadur Singh   +3 more
wiley   +1 more source