Computational and machine learning tools for insights into conjugated materials

Friday, David Mark

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https://hdl.handle.net/2142/129398 Copy

Description

Title

Computational and machine learning tools for insights into conjugated materials

Author(s)

Friday, David Mark

Issue Date

2025-04-16

Director of Research (if dissertation) or Advisor (if thesis)

Jackson, Nicholas E

Doctoral Committee Chair(s)

Jackson, Nicholas E

Committee Member(s)

• Diao, Ying
• Luthey-Schulten, Zaida
• Sing, Charles

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Conjugated Materials
• Machine Learning
• Molecular Dynamics
• Coarse Grain
• Quantum Mechanics
• Conjugated Polyelectrolyte
• Photostability

Abstract

Conjugated materials are a versatile class of electro-active organic materials with applications in energy, health, and computing. Central to improving their performance and enabling the implementation in devices is understanding their electronic properties (e.g. electronic mobility and photo-activity). These properties depend on quantum mechanical (QM) properties traditionally computed via DFT. However, DFT is unable to access the large length scales required to predict morphological-dependent properties (e.g. mobility), and the connection between QM-calculable properties and experimentally relevant molecular properties is not always clear. This work seeks to address these limitations by both developing new computational methods enabling prediction of morphologies and QM-informed electronic properties at experimentally relevant length scales, and methods to discover mechanistic insights from experimental campaigns via machine learning. The application of these methods elucidates novel mechanisms driving electronic mobility and photostability of conjugated materials, enabling their further optimization for future applications.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129398

Copyright and License Information

© 2025 David Mark Friday

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