GAUSSIAN

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: http://www.psc.edu/)


References in zbMATH (referenced in 190 articles )

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  1. Flores-Gallegos, N.: (q)-Rényi’s entropy as a possible measure of electron correlation (2021)
  2. Guo, Theron; Rokoš, Ondřej; Veroy, Karen: Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (2021)
  3. Higham, Nicholas J.; Liu, Xiaobo: A multiprecision derivative-free Schur-Parlett algorithm for computing matrix functions (2021)
  4. Kuramshina, Gulnara M.; Kochikov, Igor V.; Sharapova, Svetlana A.: Regularized \textitabinitio molecular force fields for key biological molecules: melatonin and pyridoxal-5’-phosphate methylamine Shiff base (Vitamin B6) (2021)
  5. Qiao, Wanli: Asymptotic confidence regions for density ridges (2021)
  6. Robert A. Shaw; J. Grant Hill: libecpint: A C++ library for the effcient evaluation of integrals over effective core potentials (2021) not zbMATH
  7. 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
  8. Baudoin, Fabrice; Feng, Qi; Ouyang, Cheng: Density of the signature process of fBm (2020)
  9. Chen, Houxian; Liu, Menglin; Yan, Tianying: Molecular multipoles and (hyper)polarizabilities from the Buckingham expansion: revisited (2020)
  10. Dede, Yavuz; Yalcin, Soydan; Buyuktemiz, Muhammed: Excited state structures projected onto two dimensions: correlations with luminescent behavior (2020)
  11. Ganti, Himakar; Khare, Prashant: Data-driven surrogate modeling of multiphase flows using machine learning techniques (2020)
  12. Gatti, Filippo; Clouteau, Didier: Towards blending physics-based numerical simulations and seismic databases using generative adversarial network (2020)
  13. Gerhold, Stefan; Gerstenecker, Christoph: Large deviations related to the law of the iterated logarithm for Itô diffusions (2020)
  14. Jasnovidov, Grigori: Approximation of ruin probability and ruin time in discrete Brownian risk models (2020)
  15. 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)
  16. Kuramshina, Gulnara M.; Zakharov, Alexander A.: Stable numerical methods for determination of the molecular clusters force fields (2020)
  17. Letac, Gérard; Massam, Hélène: Gaussian approximation of Gaussian scale mixtures. (2020)
  18. Morrow, Zachary; Stoyanov, Miroslav: A method for dimensionally adaptive sparse trigonometric interpolation of periodic functions (2020)
  19. Rontsis, Nikitas; Osborne, Michael A.; Goulart, Paul J.: Distributionally ambiguous optimization for batch Bayesian optimization (2020)
  20. Samanta, Bidisha; De, Abir; Jana, Gourhari; Gómez, Vicenç; Chattaraj, Pratim; Ganguly, Niloy; Gomez-Rodriguez, Manuel: \textscNeVAE: a deep generative model for molecular graphs (2020)

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