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AtomDNN : = A New Simulation Tool for Atomistic Modeling with Machine Learning Potential.

Record Type:	Electronic resources : Monograph/item
Title/Author:	AtomDNN :/
Reminder of title:	A New Simulation Tool for Atomistic Modeling with Machine Learning Potential.
Author:	Kubena, Colton.
Description:	1 online resource (56 pages)
Notes:	Source: Masters Abstracts International, Volume: 82-12.
Contained By:	Masters Abstracts International82-12.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28497015click for full text (PQDT)
ISBN:	9798516060076

AtomDNN : = A New Simulation Tool for Atomistic Modeling with Machine Learning Potential.
Kubena, Colton.

AtomDNN :A New Simulation Tool for Atomistic Modeling with Machine Learning Potential. - 1 online resource (56 pages)

Source: Masters Abstracts International, Volume: 82-12.

Thesis (M.S.)--The University of Texas at San Antonio, 2021.

Includes bibliographical references

Traditional atomistic modeling fall into two broad categories: one based on quantum mechanics methods (e.g., Density Functional Theory), and the other based on empirical interatomic potentials. The former one is accurate but computationally demanding, limited to nanometers in length scale. The later one offers much more efficient simulations but less accurate, confined by the parametrization functions used in the potentials. This has always been the hurdle when people intend to get reliable modeling results for large material systems with atomistic details. The machine learning based potential has shown great promise to address the challenges posted. Machine learning potentials are not relying on a physical functional form, but instead learn the physical shape of the energy landscape from the training dataset.We found three key technical challenges in the existing tools, which prevent the practical application of machine learning potential for large material systems:• the computational efficiency of generating atom descriptors is low. • most of the existing tools are not directly integrated to widely used atomistic modeling package LAMMPS (Large-scale Atomic Massively Parallel Simulator).• stress calculations for periodic solid-state systems is not correct in many existing tools.To overcome the challenges, we developed a new tool, called AtomDNN, which has the following features:• use Tensorflow2 platform to train a deep neuron network (DNN) based potential.• compute atom descriptors with LAMMPS, taking advantage of the already built in high efficient parallel algorithm in LAMMPS.• organic integration with LAMMPS without computation overhead.•rigorous evaluation of stress. As an example, AtomDNN has been applied to train a two dimensional material MoTe2.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798516060076Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

542853
Electronic books.

AtomDNN : = A New Simulation Tool for Atomistic Modeling with Machine Learning Potential.
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100 1 $a Kubena, Colton. $3 3693696
245 1 0 $a AtomDNN : $b A New Simulation Tool for Atomistic Modeling with Machine Learning Potential.
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500 $a Source: Masters Abstracts International, Volume: 82-12.
500 $a Advisor: Gao, Wei.
502 $a Thesis (M.S.)--The University of Texas at San Antonio, 2021.
504 $a Includes bibliographical references
520 $a Traditional atomistic modeling fall into two broad categories: one based on quantum mechanics methods (e.g., Density Functional Theory), and the other based on empirical interatomic potentials. The former one is accurate but computationally demanding, limited to nanometers in length scale. The later one offers much more efficient simulations but less accurate, confined by the parametrization functions used in the potentials. This has always been the hurdle when people intend to get reliable modeling results for large material systems with atomistic details. The machine learning based potential has shown great promise to address the challenges posted. Machine learning potentials are not relying on a physical functional form, but instead learn the physical shape of the energy landscape from the training dataset.We found three key technical challenges in the existing tools, which prevent the practical application of machine learning potential for large material systems:• the computational efficiency of generating atom descriptors is low. • most of the existing tools are not directly integrated to widely used atomistic modeling package LAMMPS (Large-scale Atomic Massively Parallel Simulator).• stress calculations for periodic solid-state systems is not correct in many existing tools.To overcome the challenges, we developed a new tool, called AtomDNN, which has the following features:• use Tensorflow2 platform to train a deep neuron network (DNN) based potential.• compute atom descriptors with LAMMPS, taking advantage of the already built in high efficient parallel algorithm in LAMMPS.• organic integration with LAMMPS without computation overhead.•rigorous evaluation of stress. As an example, AtomDNN has been applied to train a two dimensional material MoTe2.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a Energy. $3 876794
650 4 $a Artificial intelligence. $3 516317
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710 2 $a The University of Texas at San Antonio. $b Mechanical Engineering. $3 2103934
773 0 $t Masters Abstracts International $g 82-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28497015 $z click for full text (PQDT)