2024
Johnson, Holly M.; Gusev, Filipp; Dull, Jordan T.; Seo, Yejoon; Priestley, Rodney D.; Isayev, Olexandr; Rand, Barry P.
Discovery of Crystallizable Organic Semiconductors with Machine Learning Journal Article
In: J. Am. Chem. Soc., vol. 146, no. 31, pp. 21583–21590, 2024, ISSN: 1520-5126.
Abstract | Links | BibTeX | Tags: Active learning, Crystal structure
@article{Johnson2024,
title = {Discovery of Crystallizable Organic Semiconductors with Machine Learning},
author = {Holly M. Johnson and Filipp Gusev and Jordan T. Dull and Yejoon Seo and Rodney D. Priestley and Olexandr Isayev and Barry P. Rand},
url = {https://olexandrisayev.com/wp-content/uploads/johnson-et-al-2024-discovery-of-crystallizable-organic-semiconductors-with-machine-learning-1.pdf},
doi = {10.1021/jacs.4c05245},
issn = {1520-5126},
year = {2024},
date = {2024-08-07},
urldate = {2024-08-07},
journal = {J. Am. Chem. Soc.},
volume = {146},
number = {31},
pages = {21583--21590},
publisher = {American Chemical Society (ACS)},
abstract = {Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.},
keywords = {Active learning, Crystal structure},
pubstate = {published},
tppubtype = {article}
}
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison to their amorphous counterparts. Certain organic molecular thin films can be transitioned from initially prepared amorphous layers to large-scale crystalline films via abrupt thermal annealing. Ideally, these films crystallize as platelets with long-range-ordered domains on the scale of tens to hundreds of microns. However, other organic molecular thin films may instead crystallize as spherulites or resist crystallization entirely. Organic molecules that have the capability of transforming into a platelet morphology feature both high melting point (Tm) and crystallization driving force (ΔGc). In this work, we employed machine learning (ML) to identify candidate organic materials with the potential to crystallize into platelets by estimating the aforementioned thermal properties. Six organic molecules identified by the ML algorithm were experimentally evaluated; three crystallized as platelets, one crystallized as a spherulite, and two resisted thin film crystallization. These results demonstrate a successful application of ML in the scope of predicting thermal properties of organic molecules and reinforce the principles of Tm and ΔGc as metrics that aid in predicting the crystallization behavior of organic thin films.
2023
Moayedpour, Saeed; Bier, Imanuel; Wen, Wen; Dardzinski, Derek; Isayev, Olexandr; Marom, Noa
Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF) Journal Article
In: J. Phys. Chem. C, vol. 127, no. 21, pp. 10398–10410, 2023.
Abstract | Links | BibTeX | Tags: Crystal structure, Machine learning potential
@article{Moayedpour2023,
title = {Structure Prediction of Epitaxial Organic Interfaces with Ogre, Demonstrated for Tetracyanoquinodimethane (TCNQ) on Tetrathiafulvalene (TTF)},
author = {Saeed Moayedpour and Imanuel Bier and Wen Wen and Derek Dardzinski and Olexandr Isayev and Noa Marom},
doi = {10.1021/acs.jpcc.3c02384},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {J. Phys. Chem. C},
volume = {127},
number = {21},
pages = {10398--10410},
publisher = {American Chemical Society (ACS)},
abstract = {Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells and transistors, thanks to their well-controlled, uniform electronic properties and high carrier mobilities. The electronic structure of epitaxial organic interfaces and their functionality in devices are inextricably linked to their structure. We present a method for structure prediction of epitaxial organic interfaces based on lattice matching followed by surface matching, implemented in the open-source Python package, Ogre. The lattice matching step produces domain-matched interfaces, where commensurability is achieved with different integer multiples of the substrate and film unit cells. In the surface matching step, Bayesian optimization (BO) is used to find the interfacial distance and registry between the substrate and film. The BO objective function is based on dispersion corrected deep neural network interatomic potentials. These are shown to be in qualitative agreement with density functional theory (DFT) regarding the optimal position of the film on top of the substrate and the ranking of putative interface structures. Ogre is used to investigate the epitaxial interface of 7,7,8,8-tetracyanoquinodimethane (TCNQ) on tetrathiafulvalene (TTF), whose electronic structure has been probed by ultraviolet photoemission spectroscopy (UPS), but whose structure had been hitherto unknown [Organic Electronics 2017, 48, 371]. We find that TCNQ(001) on top of TTF(100) is the most stable interface configuration, closely followed by TCNQ(010) on top of TTF(100). The density of states, calculated using DFT, is in excellent agreement with UPS, including the presence of an interface charge transfer state.},
keywords = {Crystal structure, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells and transistors, thanks to their well-controlled, uniform electronic properties and high carrier mobilities. The electronic structure of epitaxial organic interfaces and their functionality in devices are inextricably linked to their structure. We present a method for structure prediction of epitaxial organic interfaces based on lattice matching followed by surface matching, implemented in the open-source Python package, Ogre. The lattice matching step produces domain-matched interfaces, where commensurability is achieved with different integer multiples of the substrate and film unit cells. In the surface matching step, Bayesian optimization (BO) is used to find the interfacial distance and registry between the substrate and film. The BO objective function is based on dispersion corrected deep neural network interatomic potentials. These are shown to be in qualitative agreement with density functional theory (DFT) regarding the optimal position of the film on top of the substrate and the ranking of putative interface structures. Ogre is used to investigate the epitaxial interface of 7,7,8,8-tetracyanoquinodimethane (TCNQ) on tetrathiafulvalene (TTF), whose electronic structure has been probed by ultraviolet photoemission spectroscopy (UPS), but whose structure had been hitherto unknown [Organic Electronics 2017, 48, 371]. We find that TCNQ(001) on top of TTF(100) is the most stable interface configuration, closely followed by TCNQ(010) on top of TTF(100). The density of states, calculated using DFT, is in excellent agreement with UPS, including the presence of an interface charge transfer state.