Computational and machine learning tools for insights into conjugated materials

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemistry

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Friday, David Mark

Contributors dc:contributor

• Jackson, Nicholas E
• Diao, Ying
• Luthey-Schulten, Zaida
• Sing, Charles

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

Statement dc:rights

• © 2025 David Mark Friday

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129398