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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 +2 more
wiley +1 more source
Towards Advanced Intelligent and Perceptive Soft Grippers
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim +4 more
wiley +1 more source
Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System
This work introduces a memristor‐based reservoir computing (RC) system for real‐time, energy‐efficient processing of neural signals in brain‐machine interface (BMI). Combined with flexible mesh neural probes with tissue‐like flexibility and subcellular‐scale features that enable consistent, long‐term tracking of single‐cell neural activities, the ...
Soohyeon Kim +10 more
wiley +1 more source
Climate change and perennial crop production: Evidence of yield impact and adaptation in California
Abstract Perennial crops are economically important. They contribute to food security, providing essential nutrients that are often lacking in annual crops, and provide additional environmental benefits compared with annual crops. Despite their importance, empirical research on the impacts of climate change and adaptation on perennial crops remains ...
Yuanyuan Wen +2 more
wiley +1 more source
Abstract Although peer support is central to the social model approach emphasized in sober living houses (SLHs), no longitudinal studies have examined helping among SLH residents. This longitudinal study examined benefits of helping in three contexts among SLH residents. Data were from 205 participants entering 28 SLHs across 2021–2023. Interviews were
Sarah E. Zemore +4 more
wiley +1 more source
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Computational complexity of spin-glass three-dimensional (3D) Ising model
Journal of Materials Science and Technology, 2020In this work, the computational complexity of a spin-glass three-dimensional (3D) Ising model (for the lattice size N = lmn, where l, m, n are the numbers of lattice points along three crystallographic directions) is studied.
Zhidong Zhang
exaly +2 more sources
Physical Review B, 1988
In this paper we describe a numerical method of solving the local-density mean-field equations of spatially frustrated lattice models. Using techniques of simulated annealing and simultaneous updating of collective modes, we have been able to locate the global minima of a function with many nearby local minima.
, Balbuena, , Dawson
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In this paper we describe a numerical method of solving the local-density mean-field equations of spatially frustrated lattice models. Using techniques of simulated annealing and simultaneous updating of collective modes, we have been able to locate the global minima of a function with many nearby local minima.
, Balbuena, , Dawson
openaire +2 more sources
Physica A: Statistical Mechanics and its Applications, 2019
The magnetic properties of mixed spin-5/2 and spin-2 Ising model on a decorated square lattice are studied using the Monte Carlo simulations. The ground state phase diagrams of mixed spin-5/2 and spin-2 Ising model on a decorated square lattice are ...
A. Jabar, R. Masrour
semanticscholar +1 more source
The magnetic properties of mixed spin-5/2 and spin-2 Ising model on a decorated square lattice are studied using the Monte Carlo simulations. The ground state phase diagrams of mixed spin-5/2 and spin-2 Ising model on a decorated square lattice are ...
A. Jabar, R. Masrour
semanticscholar +1 more source
ON ISING'S MODEL OF FERROMAGNETISM
Mathematical Proceedings of the Cambridge Philosophical Society, 1936Ising discussed the following model of a ferromagnetic body: Assume N elementary magnets of moment μ to be arranged in a regular lattice; each of them is supposed to have only two possible orientations, which we call positive and negative. Assume further that there is an interaction energy U for each pair of neighbouring magnets of opposite direction ...
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