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Development of Machine Learning Pote...

Zeng, Jinzhe,

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Development of Machine Learning Potentials for Biochemical Systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Development of Machine Learning Potentials for Biochemical Systems // Jinzhe Zeng.
作者:	Zeng, Jinzhe,
面頁冊數:	1 electronic resource (288 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
標題:	Chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31764095
ISBN:	9798304911917

Development of Machine Learning Potentials for Biochemical Systems /
Zeng, Jinzhe,

Development of Machine Learning Potentials for Biochemical Systems /Jinzhe Zeng. - 1 electronic resource (288 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

MLP models present a compelling alternative to traditional computational methods, offering significantly faster performance than ab initio quantum mechanics (QM) methods and higher accuracy than classical force fields, making them particularly suitable for molecular dynamics (MD) simulations of large biochemical systems. In this dissertation, several developments of machine learning potential (MLP) models tailored for biochemical systems are presented. Chapter 3 introduces the Deep Potential Range Correction (DPRc) model to construct efficient and accurate potential energy models for free energy profiles of biochemical reactions in solution. Chapter 4 and chapter 5 propose the Quantum Deep Potential (QDπ) model particularly for drug discovery applications and show its high accuracy. Chapter 6 details the development of DeePMD-kit (version 2), a software suite designed for constructing and deploying MLP models. Chapter 7 introduces the QDπ dataset, comprising high-precision potential energy data for small organic molecules. Finally, chapter 8 presents DeePMD-GNN, a software package enabling the integration of graph neural network (GNN)-based models, within the DeePMD-kit framework. Taken together, this work advances the state-of-the-art in computational chemistry, offering novel methodologies and tools for accurate and efficient modeling of complex biochemical systems.

English

ISBN: 9798304911917Subjects--Topical Terms:

516420
Chemistry.
Subjects--Index Terms:

Biochemical systems

Development of Machine Learning Potentials for Biochemical Systems /
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520 $a MLP models present a compelling alternative to traditional computational methods, offering significantly faster performance than ab initio quantum mechanics (QM) methods and higher accuracy than classical force fields, making them particularly suitable for molecular dynamics (MD) simulations of large biochemical systems. In this dissertation, several developments of machine learning potential (MLP) models tailored for biochemical systems are presented. Chapter 3 introduces the Deep Potential Range Correction (DPRc) model to construct efficient and accurate potential energy models for free energy profiles of biochemical reactions in solution. Chapter 4 and chapter 5 propose the Quantum Deep Potential (QDπ) model particularly for drug discovery applications and show its high accuracy. Chapter 6 details the development of DeePMD-kit (version 2), a software suite designed for constructing and deploying MLP models. Chapter 7 introduces the QDπ dataset, comprising high-precision potential energy data for small organic molecules. Finally, chapter 8 presents DeePMD-GNN, a software package enabling the integration of graph neural network (GNN)-based models, within the DeePMD-kit framework. Taken together, this work advances the state-of-the-art in computational chemistry, offering novel methodologies and tools for accurate and efficient modeling of complex biochemical systems.
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