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High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

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    Physical Insights into Materials and Molecular Properties

    High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set
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    The Journal of Physical Chemistry Letters

    Cite this: J. Phys. Chem. Lett. 2022, 13, 13, 2934–2942
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    https://doi.org/10.1021/acs.jpclett.2c00453
    Published March 28, 2022
    Copyright © 2022 American Chemical Society
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    Abstract

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    A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

    Copyright © 2022 American Chemical Society

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    Supporting Information

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    • Details about DFTB calculations, the ChIMES functional form, the fitting procedure, and ChIMES hyperparameter selection (including radial ranges, polynomial orders, and other pertinent details) (PDF)

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    This article is cited by 17 publications.

    1. Riccardo Dettori, Nir Goldman. Creation of an Fe3P Schreibersite Density Functional Tight Binding Model for Astrobiological Simulations. The Journal of Physical Chemistry A 2025, 129 (2) , 583-595. https://doi.org/10.1021/acs.jpca.4c05881
    2. Bing Huang, O. Anatole von Lilienfeld, Jaron T. Krogel, Anouar Benali. Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML. Journal of Chemical Theory and Computation 2023, 19 (6) , 1711-1721. https://doi.org/10.1021/acs.jctc.2c01058
    3. Timothy J. Giese, Jinzhe Zeng, Darrin M. York. Multireference Generalization of the Weighted Thermodynamic Perturbation Method. The Journal of Physical Chemistry A 2022, 126 (45) , 8519-8533. https://doi.org/10.1021/acs.jpca.2c06201
    4. Matthew P. Kroonblawd, Anthony Yoshimura, Nir Goldman, Amitesh Maiti, James P. Lewicki, Andrew P. Saab. Multiscale Strategy for Predicting Radiation Chemistry in Polymers. Journal of Chemical Theory and Computation 2022, 18 (9) , 5117-5124. https://doi.org/10.1021/acs.jctc.2c00582
    5. Rebecca K. Lindsey, Sorin Bastea, Sebastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi. ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data. npj Computational Materials 2025, 11 (1) https://doi.org/10.1038/s41524-024-01497-y
    6. Nathaniel Troup, Matthew P. Kroonblawd, Davide Donadio, Nir Goldman. Quantum Simulations of Radiation Damage in a Molecular Polyethylene Analog. Macromolecular Rapid Communications 2024, 45 (24) https://doi.org/10.1002/marc.202400669
    7. Yinuo Yang, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev, Adrian E. Roitberg. Machine Learning of Reactive Potentials. Annual Review of Physical Chemistry 2024, 75 (1) , 371-395. https://doi.org/10.1146/annurev-physchem-062123-024417
    8. Pavlo O. Dral. AI in computational chemistry through the lens of a decade-long journey. Chemical Communications 2024, 60 (24) , 3240-3258. https://doi.org/10.1039/D4CC00010B
    9. Artem Soshnikov, Rebecca Lindsey, Ambarish Kulkarni, Nir Goldman. A reactive molecular dynamics model for uranium/hydrogen containing systems. The Journal of Chemical Physics 2024, 160 (9) https://doi.org/10.1063/5.0183610
    10. Jia-Hao 佳豪 Xiong 熊, Zi-Jun 梓俊 Qi 戚, Kang 康 Liang 梁, Xiang 祥 Sun 孙, Zhan-Peng 展鹏 Sun 孙, Qi-Jun 启军 Wang 汪, Li-Wei 黎玮 Chen 陈, Gai 改 Wu 吴, Wei 威 Shen 沈. Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential. Chinese Physics B 2023, 32 (12) , 128101. https://doi.org/10.1088/1674-1056/ace4b4
    11. C. Panosetti, Y. Lee, A. Samtsevych, C. Scheurer. Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide. The Journal of Chemical Physics 2023, 158 (22) https://doi.org/10.1063/5.0141233
    12. Nir Goldman, Laurence E. Fried, Rebecca K. Lindsey, C. Huy Pham, R. Dettori. Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials. The Journal of Chemical Physics 2023, 158 (14) https://doi.org/10.1063/5.0141616
    13. Jinzhe Zeng, Yujun Tao, Timothy J. Giese, Darrin M. York. Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states. The Journal of Chemical Physics 2023, 158 (12) https://doi.org/10.1063/5.0139281
    14. Rodrigo A. Vargas–Hernández, Kjell Jorner, Robert Pollice, Alán Aspuru–Guzik. Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation. The Journal of Chemical Physics 2023, 158 (10) https://doi.org/10.1063/5.0137103
    15. Qiang Zhu, Shinnosuke Hattori. Organic crystal structure prediction and its application to materials design. Journal of Materials Research 2023, 38 (1) , 19-36. https://doi.org/10.1557/s43578-022-00698-9
    16. Pavlo O. Dral, Tetiana Zubatiuk. Improving semiempirical quantum mechanical methods with machine learning. 2023, 559-575. https://doi.org/10.1016/B978-0-323-90049-2.00014-7
    17. Nir Goldman, Luis Zepeda-Ruiz, Ryan G. Mullen, Rebecca K. Lindsey, C. Huy Pham, Laurence E. Fried, Jonathan L. Belof. Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO2. Applied Sciences 2022, 12 (21) , 11005. https://doi.org/10.3390/app122111005
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    The Journal of Physical Chemistry Letters

    Cite this: J. Phys. Chem. Lett. 2022, 13, 13, 2934–2942
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpclett.2c00453
    Published March 28, 2022
    Copyright © 2022 American Chemical Society
    Request reuse permissions

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