scholarly journals Direct Path-Integral Scheme for Fatigue Simulation of Reinforced Concrete in Shear

2006 ◽  
Vol 4 (1) ◽  
pp. 159-177 ◽  
Author(s):  
Koichi Maekawa ◽  
Kukrit Toongoenthong ◽  
Esayas Gebreyouhannes ◽  
Toshiharu Kishi
Keyword(s):  
Reinforced Concrete ◽  
Path Integral ◽  
Direct Path ◽  
Integral Scheme

scholarly journals Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

2021 ◽  
Author(s):  
Chenghan Li ◽  
Gregory A. Voth
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Computational Cost ◽  
Ab Initio Molecular Dynamics ◽  
Direct Path ◽  
Widespread Application ◽  
Path Integral Representation ◽  
Order Of Magnitude

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-order-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

Direct path-integral treatment of the polaron problem

1983 ◽  
Vol 27 (8) ◽  
pp. 4586-4600 ◽  
Author(s):  
A. L. Kholodenko ◽  
Karl F. Freed
Keyword(s):  
Path Integral ◽  
Direct Path ◽  
Integral Treatment

scholarly journals Direct path integral estimators for isotope fractionation ratios

2014 ◽  
Vol 141 (24) ◽  
pp. 244112 ◽  
Author(s):  
Bingqing Cheng ◽  
Michele Ceriotti
Keyword(s):  
Path Integral ◽  
Isotope Fractionation ◽  
Direct Path

scholarly journals Using Machine Learning to Greatly Accelerate Path Integral Ab Initio Molecular Dynamics

2022 ◽  
Author(s):  
Chenghan Li ◽  
Gregory A. Voth
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Path Integral ◽  
Computational Cost ◽  
Computational Approach ◽  
Ab Initio Molecular Dynamics ◽  
Direct Path ◽  
Widespread Application ◽  
Path Integral Representation

Ab initio molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a two-orders-of-magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

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