자료유형  

 학위논문

Control Number  

0017163957

International Standard Book Number  

9798384098614

Dewey Decimal Classification Number  

540

Main Entry-Personal Name  

Kirkvold, Clara.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Minnesota., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

136 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Goodpaster, Jason D.

Dissertation Note  

Thesis (Ph.D.)--University of Minnesota, 2024.

Summary, Etc.  

요약Machine learning has been widely applied to accelerate molecular simulations and predict molecular/material properties. Machine learning accomplishes this by leveraging the patterns and relationships between a system's features and the desired property. To further the application of machine learning in chemistry, developing new algorithms and featurization techniques is vital. This dissertation presents innovative machine-learning frameworks and featurization techniques to predict a variety of molecular/material properties and accelerate molecular simulations. In Chapter 2, we investigate training neural networks on features built from information obtained from cheap computational electronic structure (e.g., Hartree-Fock) calculations to predict more expensive ab initio calculations. Chapter 3 presents the development of a machine learning framework that combines neural networks with the many-body expanded Full Configuration Interaction method. In Chapter 4, we apply featurization techniques inspired by natural language processing to leverage nominal categorical data for predicting adsorption energies on metallic surfaces at the Density Functional Theory level. Finally, Chapter 5 introduces a novel hybrid Neural Network Potential/Molecular Mechanics algorithm. Overall, this work provides significant insight into developing more efficient and accurate machine-learning methods for understanding and predicting molecular and material properties.

Subject Added Entry-Topical Term  

Chemistry.

Subject Added Entry-Topical Term  

Physical chemistry.

Subject Added Entry-Topical Term  

Computational chemistry.

Index Term-Uncontrolled  

Catalysis

Index Term-Uncontrolled  

Electronic structure

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Embedding networks

Index Term-Uncontrolled  

Natural language processing

Added Entry-Corporate Name  

University of Minnesota Chemistry

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:656853