Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

Wang, Chun-I; Maier, J. Charlie; Jackson, Nicholas E.

doi:10.1039/d3sc06749a

Chem. Sci.

2024

DOI: 10.1039/d3sc06749a

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Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

Chun-I Wang,

J. Charlie Maier,

Nicholas E. Jackson

Abstract: Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these multiscale relationships require the complex integration of...

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Chemically Transferable Electronic Coarse Graining for Polythiophenes

Yu,

Jackson

2024

J. Chem. Theory Comput.

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Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

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Chemically Transferable Electronic Coarse Graining for Polythiophenes

Yu,

Jackson

2024

J. Chem. Theory Comput.

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Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Cheng,

Ser,

Skreta

et al. 2025

Faraday Discuss.

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Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

Abstract: Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these multiscale relationships require the complex integration of...

Cited by 2 publications

References 158 publications

Chemically Transferable Electronic Coarse Graining for Polythiophenes

Chemically Transferable Electronic Coarse Graining for Polythiophenes

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

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