Document Type
Article
Publication Date
2026
Publication Title
ACS Physical Chemistry Au
Keywords
crystal structure prediction, particle swarm optimization, CALYPSO, density-functional tight binding, DFTB
Abstract
Over the years, computational crystal structure prediction (CSP) for organic molecules has thrived as an area of research, spanning various scientific disciplines and having significant applications in industries such as pharmaceuticals and agrochemicals. Within the field of batteries, redox-active organic materials (ROMs) such as quinones have received increased attention as promising electrode materials for rechargeable batteries. However, experimental determination of the crystal structure of intermediate species formed during the discharge/charge cycle can often be challenging. Incomplete X-ray diffraction patterns can also lead to difficulties in crystal structure determination for ROMs used in batteries. Use of a semiempirical electronic structure method for CSP helps to avoid force field reparameterization for different species, sometimes with complex electronic structure, formed during battery operation. It also helps to significantly lower the computational cost compared to the widely used density functional theory (DFT). In this study, we report the success of a CSP algorithm based on a combination of the particle swarm optimization method, as implemented in the CALYPSO software, with third-order density functional tight-binding, a DFT-based semiempirical method. Accompanied by data postprocessing using DFT, this method enables the correct identification of the most stable crystal structures of organic molecules with different kinds of intermolecular interactions ranging from hydrogen bonding to π-stacking. We also report the experimental crystal structure of pyrene-4,5,9,10-tetrone, a molecule studied intensively for application in organic batteries, and predict its crystal structure correctly using our method. Our findings emphasize the potential of this approach for CSP of different classes of organic molecules, including quinones. Additionally, they establish the foundation for future CSP studies of other organic molecules utilized in rechargeable batteries.
Funding Source
This work was supported partially by a Research Corporation for Science Advancement Scialog Collaborative Award to P. G. and Y. Y. (award # 25753 and 25751, respectively) and the Texas Advanced Computing Center (TACC) at the University of Texas at Austin as part of the Frontera Fellowship Program, funded by National Science Foundation (award #1818253). M. M. K. was a Frontera Computational Fellow in 2020–2021. Computational resources from the high performance computing center (Spiedie) at the Thomas J. Watson College of Engineering and Applied Science at Binghamton University are also acknowledged. This article was published open access thanks to a transformative agreement between Milner Library and ACS.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
10.1021/acsphyschemau.6c00036
Recommended Citation
Kitheka, M. M., Jing, Y., Yao, Y., & Goyal, P. (2026). Efficient Organic Molecular Crystal Structure Prediction Using the Density Functional Tight-Binding Method. ACS Physical Chemistry Au. https://doi.org/10.1021/acsphyschemau.6c00036
Comments
First published in ACS Physical Chemistry Au (2026): https://doi.org/10.1021/acsphyschemau.6c00036. Supplemental information available on the publisher's site.