Results 101 to 110 of about 189,782 (285)

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
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

Comparing quantum and classical Monte Carlo algorithms for estimating Betti numbers of clique complexes [PDF]

Quantum
Several quantum and classical Monte Carlo algorithms for Betti Number Estimation (BNE) on clique complexes have recently been proposed, though it is unclear how their performances compare.
Ismail Yunus Akhalwaya, Ahmed Bhayat, Adam Connolly, Steven Herbert, Lior Horesh, Julien Sorci, Shashanka Ubaru   +6 more
doaj   +1 more source

Optimal Summation and Integration by Deterministic, Randomized, and Quantum Algorithms [PDF]

, 2001
We survey old and new results about optimal algorithms for summation of finite sequences and for integration of functions from Hoelder or Sobolev spaces. First we discuss optimal deterministic and randomized algorithms.
Heinrich, S., Novak, E.
core   +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

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

Quantum Molecular Dynamics

Quantum Reports
We formulate a new quantum many-body simulation method for a general quantum fluid at any given temperature. Unlike the path integral Monte Carlo method, our method evolves, in imaginary time, the density matrix from its initial delta function condition ...
Riccardo Fantoni
doaj   +1 more source

Quantum Frenkel-Kontorova Model

, 2001
This paper gives a review of our recent work on the quantum Frenkel-Kontorova model. Using the squeezed state theory and the quantum Monte Carlo method, we have studied the effects of quantum fluctuations on the Aubry transition and the behavior of the ...
Aubry, Aubry, Aubry, Aubry, Bambi Hu, Baowen Li, Berman, Borgonovi, Borgonovi, Braun, Braun, Braun, Coppersmith, Creutz, Frank, Frenkel, Gyalog, Hu, Hu, Hu, Hu, Pattanayak, Peyrard, Rozman, Rozman, Rozman, Shuryak, Stunz, Tsue, Tsue, Weiss, Zaloj, Zhang   +32 more
core   +2 more sources

Understanding Quantum Tunneling through Quantum Monte Carlo Simulations

Physical Review Letters, 2016
5 pages, 4 figures, 10 pages of supplemental ...
Isakov, Sergei V., Mazzola, Guglielmo, Smelyanskiy, Vadim N., Jiang, Zhang, Boixo, Sergio, Neven, Hartmut, Troyer, Matthias   +6 more
openaire   +4 more sources

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source