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Machine Learning Towards Large-Scale...

Zuo, Yunxing.

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Machine Learning Towards Large-Scale Atomistic Simulation and Materials Discovery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Towards Large-Scale Atomistic Simulation and Materials Discovery./
作者:	Zuo, Yunxing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	141 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Computational chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28542675
ISBN:	9798460409969

Machine Learning Towards Large-Scale Atomistic Simulation and Materials Discovery.
Zuo, Yunxing.

Machine Learning Towards Large-Scale Atomistic Simulation and Materials Discovery. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 141 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2021.

This item must not be sold to any third party vendors.

In materials science, the first principles modeling, especially density functional theory (DFT), serves as the de facto tool in studying physical phenomena and properties of materials from the atomistic level. However, the high computational cost and poor scaling of DFT has limited its applications in two important scientific problems - large-scale atomistic simulations and high-throughput screening for materials discovery. This thesis demonstrates how the machine learning (ML) techniques enable atomistic simulations in large size and time scale with DFT-accuracy and accelerate materials discovery with the state-of-the-art graph neural network models. This thesis is divided into two topics.In the first topic (Chapters 2 and 3), we will investigate how the machine learning interatomic potentials (ML-IAPs) are trained and provide a systematic assessment of the cost and accuracy performances for several major ML-IAPs. We have also implemented high-level Python interfaces for ML-IAPs development and materials properties calculators using a molecular dynamic (MD) engine. This toolkit enabled us to develop a highly accurate and efficient ML-IAP for refractory high-entropy alloy NbMoTaW, an important alloy system yielding exceptional mechanical properties under high temperature. We will demonstrate how the ML-IAP driven atomistic simulations help us understand the mobility of edge/screw dislocations with the presence of short-range order (SRO).In the second topic (Chapter 4), we developed a Bayesian Optimization With Symmetry Relaxation (BOWSR) algorithm using MatErials Graph Network (MEGNet) energy model to obtain equilibrium crystal structures, bypassing the high-cost DFT relaxations. The BOWSR algorithm enabled us to screen ∼ 400,000 transition metal borides and carbides for ultra-incompressible hard materials. Attempts were made to synthesize the top ten candidates with the highest computed bulk modulus with eight unique compositions, and two new crystals yielding ultra-incompressibility were successfully synthesized.

ISBN: 9798460409969Subjects--Topical Terms:

3350019
Computational chemistry.
Subjects--Index Terms:

large-scale atomistic simulation

Machine Learning Towards Large-Scale Atomistic Simulation and Materials Discovery.
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500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
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502 $a Thesis (Ph.D.)--University of California, San Diego, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In materials science, the first principles modeling, especially density functional theory (DFT), serves as the de facto tool in studying physical phenomena and properties of materials from the atomistic level. However, the high computational cost and poor scaling of DFT has limited its applications in two important scientific problems - large-scale atomistic simulations and high-throughput screening for materials discovery. This thesis demonstrates how the machine learning (ML) techniques enable atomistic simulations in large size and time scale with DFT-accuracy and accelerate materials discovery with the state-of-the-art graph neural network models. This thesis is divided into two topics.In the first topic (Chapters 2 and 3), we will investigate how the machine learning interatomic potentials (ML-IAPs) are trained and provide a systematic assessment of the cost and accuracy performances for several major ML-IAPs. We have also implemented high-level Python interfaces for ML-IAPs development and materials properties calculators using a molecular dynamic (MD) engine. This toolkit enabled us to develop a highly accurate and efficient ML-IAP for refractory high-entropy alloy NbMoTaW, an important alloy system yielding exceptional mechanical properties under high temperature. We will demonstrate how the ML-IAP driven atomistic simulations help us understand the mobility of edge/screw dislocations with the presence of short-range order (SRO).In the second topic (Chapter 4), we developed a Bayesian Optimization With Symmetry Relaxation (BOWSR) algorithm using MatErials Graph Network (MEGNet) energy model to obtain equilibrium crystal structures, bypassing the high-cost DFT relaxations. The BOWSR algorithm enabled us to screen ∼ 400,000 transition metal borides and carbides for ultra-incompressible hard materials. Attempts were made to synthesize the top ten candidates with the highest computed bulk modulus with eight unique compositions, and two new crystals yielding ultra-incompressibility were successfully synthesized.
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