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 172 articles )

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  1. Gerhold, Stefan; Gerstenecker, Christoph: Large deviations related to the law of the iterated logarithm for Itô diffusions (2020)
  2. 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)
  3. 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
  4. Chen, Xiaojun; Kelley, C. T.: Convergence of the EDIIS algorithm for nonlinear equations (2019)
  5. Ji, Lanpeng; Liu, Peng; Robert, Stephan: Tail asymptotic behavior of the supremum of a class of chi-square processes (2019)
  6. Kristofer Björnson: TBTK: A quantum mechanics software development kit (2019) not zbMATH
  7. Saravanan, Kandasamy; Kumaradhas, Poomani: Acylguanidine-BACE1 complex: insights of intermolecular interactions and dynamics (2019)
  8. 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
  9. Bai, Long; Dȩbicki, Krzysztof; Hashorva, Enkelejd; Luo, Li: On generalised Piterbarg constants (2018)
  10. Bodroski, Zarko; Vukmirović, Nenad; Skrbic, Srdjan: Gaussian basis implementation of the charge patching method (2018)
  11. 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)
  12. Feragen, Aasa (ed.); Hotz, Thomas (ed.); Huckemann, Stephan (ed.); Miller, Ezra (ed.): Statistics for data with geometric structure. Abstracts from the workshop held January 21--27, 2018 (2018)
  13. Hashorva, Enkelejd: Representations of (\max)-stable processes via exponential tilting (2018)
  14. Ivanovs, Jevgenijs: Zooming in on a Lévy process at its supremum (2018)
  15. Kelley, C. T.: Numerical methods for nonlinear equations (2018)
  16. Sottinen, Tommi; Viitasaari, Lauri: Conditional-mean hedging under transaction costs in Gaussian models (2018)
  17. Wang, Yizao: Extremes of (q)-Ornstein-Uhlenbeck processes (2018)
  18. Avery, Patrick; Falls, Zackary; Zurek, Eva: \textscXtalOptversion r10: an open-source evolutionary algorithm for crystal structure prediction (2017)
  19. Gyevi-Nagy, László; Tasi, Gyula: SYVA: a program to analyze symmetry of molecules based on vector algebra (2017)
  20. Hamdi, Hamidreza; Couckuyt, Ivo; Sousa, Mario Costa; Dhaene, Tom: Gaussian processes for history-matching: application to an unconventional gas reservoir (2017)

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