Results 161 to 170 of about 637,276 (316)

From Monte Carlo to quantum computation [PDF]

Mathematics and Computers in Simulation, 2003
Quantum computing was so far mainly concerned with discrete problems. Recently, E. Novak and the author studied quantum algorithms for high dimensional integration and dealt with the question, which advantages quantum computing can bring over classical deterministic or randomized methods for this type of problem.
openaire   +2 more sources

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model

, 2018
Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems.
Batrouni, George, Chen, Chuang, Liu, Junwei, Meng, Zi Yang, Scalettar, Richard, Xu, Xiao Yan   +5 more
core   +3 more sources

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Introduction to the variational and diffusion Monte Carlo methods

, 2015
We provide a pedagogical introduction to the two main variants of real-space quantum Monte Carlo methods for electronic-structure calculations: variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC).
Anderson, Anderson, Anderson, Assaraf, Assaraf, Assaraf, Assaraf, Assaraf, Austin, Badinski, Buonaura, Caffarel, Caflisch, Ceperley, Ceperley, Ceperley, Conroy, Foulkes, Gilks, Grimm, Hammond, Hastings, Kato, Klein, Kolorenč, Lee, López-Rios, McMillan, Melko, Metropolis, Moskowitz, Nightingale, Nightingale, Nightingale, Pack, Petruzielo, Press, Reynolds, Reynolds, Rothstein, Rothstein, Schautz, Schmidt, Sorella, Toulouse, Toulouse, Toulouse, Umrigar, Umrigar, Umrigar, Umrigar, Umrigar, Umrigar, Wolff   +53 more
core   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Methods for calculating forces within quantum Monte Carlo simulations [PDF]

, 2010
A. Badinski, Peter D. Haynes, J. R. Trail, R. J. Needs   +3 more
openalex   +1 more source

A new generation of HPC developers using quantum Monte Carlo (QMC) methods is growing

, 2021
Sara Pittonet, Julie Abergas-Arteza, Jacopo Mariani   +2 more
openalex   +1 more source

Quantum speedup of Monte Carlo methods [PDF]

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2015
Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions.
openaire   +5 more sources