Amp: A modular approach to machine learning in atomistic simulations

2016 ◽  
Vol 207 ◽  
pp. 310-324 ◽  
Author(s):  
Alireza Khorshidi ◽  
Andrew A. Peterson
Keyword(s):  
Machine Learning ◽  
Atomistic Simulations ◽  
Modular Approach

Defect Dynamics in 2-D MoS2 Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy

ACS Nano ◽  
2018 ◽  
Vol 12 (8) ◽  
pp. 8006-8016 ◽  
Author(s):  
Tarak K. Patra ◽  
Fu Zhang ◽  
Daniel S. Schulman ◽  
Henry Chan ◽  
Mathew J. Cherukara ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Atomistic Simulations ◽  
High Resolution Microscopy

scholarly journals Molecular Process of Gramicidin a Dimerization Determined with Milliseconds Atomistic Simulations and Machine Learning

2020 ◽  
Vol 118 (3) ◽  
pp. 555a
Author(s):  
Delin Sun ◽  
Stewart He ◽  
W.F. Drew Bennett ◽  
Felice C. Lightstone ◽  
Helgi I. Ingólfsson
Keyword(s):  
Machine Learning ◽  
Gramicidin A ◽  
Atomistic Simulations ◽  
Molecular Process

scholarly journals Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material

Nanoscale ◽  
2021 ◽  
Author(s):  
Daniele Dragoni ◽  
Jörg Behler ◽  
Marco Bernasconi
Keyword(s):  
Machine Learning ◽  
Phase Change ◽  
Ultrathin Films ◽  
Large Scale ◽  
Interatomic Potential ◽  
Atomistic Simulations ◽  
Machine Learning Method ◽  
Learning Method ◽  
Phase Stabilization ◽  
Change Material

Large scale atomistic simulations with an interatomic potential generated by a machine learning method have been exploited to study the crystallization of Sb in ultrathin films.

scholarly journals Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations

2022 ◽  
Author(s):  
Dylan Bayerl ◽  
Christopher Michael Andolina ◽  
Shyam Dwaraknath ◽  
Wissam A Saidi
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Dft Calculations ◽  
Density Functional ◽  
Convergence Acceleration ◽  
Atomistic Simulations ◽  
Functional Theory ◽  
Materials Modeling

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy...

scholarly journals A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers

2015 ◽  
Vol 115 (16) ◽  
pp. 1129-1139 ◽  
Author(s):  
Marco Caccin ◽  
Zhenwei Li ◽  
James R. Kermode ◽  
Alessandro De Vita
Keyword(s):  
Machine Learning ◽  
Atomistic Simulations ◽  
Parallel Supercomputers
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