Gaussian is an electronic structure program, used by chemists, chemical engineers, biochemists, physicists and others for research in established and emerging areas of chemical interest. Starting from the basic laws of quantum mechanics, Gaussian predicts the energies, molecular structures, and vibrational frequencies of molecular systems, along with numerous molecular properties derived from these basic computation types. It can be used to study molecules and reactions under a wide range of conditions, including both stable species and compounds which are difficult or impossible to observe experimentally such as short-lived intermediates and transition structures. (Source:

References in zbMATH (referenced in 181 articles )

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  1. Taewon David Kim, Michael Richer, Gabriela Sánchez-Díaz, Farnaz Heidar-Zadeh, Toon Verstraelen, Ramón Alain Miranda-Quintana, Paul W. Ayers: Fanpy: A Python Library for Prototyping Multideterminant Methods in Ab Initio Quantum Chemistry (2021) arXiv
  2. Baudoin, Fabrice; Feng, Qi; Ouyang, Cheng: Density of the signature process of fBm (2020)
  3. Chen, Houxian; Liu, Menglin; Yan, Tianying: Molecular multipoles and (hyper)polarizabilities from the Buckingham expansion: revisited (2020)
  4. Gerhold, Stefan; Gerstenecker, Christoph: Large deviations related to the law of the iterated logarithm for Itô diffusions (2020)
  5. Jasnovidov, Grigori: Approximation of ruin probability and ruin time in discrete Brownian risk models (2020)
  6. Kissas, Georgios; Yang, Yibo; Hwuang, Eileen; Witschey, Walter R.; Detre, John A.; Perdikaris, Paris: Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (2020)
  7. Kuramshina, Gulnara M.; Zakharov, Alexander A.: Stable numerical methods for determination of the molecular clusters force fields (2020)
  8. Morrow, Zachary; Stoyanov, Miroslav: A method for dimensionally adaptive sparse trigonometric interpolation of periodic functions (2020)
  9. Rontsis, Nikitas; Osborne, Michael A.; Goulart, Paul J.: Distributionally ambiguous optimization for batch Bayesian optimization (2020)
  10. Samanta, Bidisha; De, Abir; Jana, Gourhari; Gómez, Vicenç; Chattaraj, Pratim; Ganguly, Niloy; Gomez-Rodriguez, Manuel: \textscNeVAE: a deep generative model for molecular graphs (2020)
  11. Xinming Qin, Honghui Shang, Lei Xu, Wei Hu, Jinlong Yang, Shigang Li, Yunquan Zhang: The static parallel distribution algorithms for hybrid density-functional calculations in HONPAS package (2020) arXiv
  12. Chen, Xiaojun; Kelley, C. T.: Convergence of the EDIIS algorithm for nonlinear equations (2019)
  13. Ji, Lanpeng; Liu, Peng; Robert, Stephan: Tail asymptotic behavior of the supremum of a class of chi-square processes (2019)
  14. Kristofer Björnson: TBTK: A quantum mechanics software development kit (2019) not zbMATH
  15. Saravanan, Kandasamy; Kumaradhas, Poomani: Acylguanidine-BACE1 complex: insights of intermolecular interactions and dynamics (2019)
  16. Yunqi Shao, Matti Hellström, Pavlin D. Mitev, Lisanne Knijff, Chao Zhang: PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials (2019) arXiv
  17. Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E: DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models (2019) arXiv
  18. Bai, Long; Dȩbicki, Krzysztof; Hashorva, Enkelejd; Luo, Li: On generalised Piterbarg constants (2018)
  19. Bodroski, Zarko; Vukmirović, Nenad; Skrbic, Srdjan: Gaussian basis implementation of the charge patching method (2018)
  20. Cimrman, Robert; Novák, Matyáš; Kolman, Radek; Tuma, Miroslav; Plešek, Jiří; Vackář, Jiří: Convergence study of isogeometric analysis based on Bézier extraction in electronic structure calculations (2018)

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