Institution
Moscow State University
Education•Moscow, Russia•
About: Moscow State University is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Catalysis & Laser. The organization has 66747 authors who have published 123358 publications receiving 1753995 citations. The organization is also known as: MSU & Lomonosov Moscow State University.
Topics: Catalysis, Laser, Population, Magnetic field, Crystal structure
Papers published on a yearly basis
Papers
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University of South Carolina1, Los Alamos National Laboratory2, Moscow State University3, Delhi Technological University4, University of Paris5, University of California, Davis6, Indian Institute of Technology (BHU) Varanasi7, University of Moratuwa8, University of Illinois at Urbana–Champaign9, California Polytechnic State University10, Sandia National Laboratories11, Max Planck Society12, Indian Institute of Technology Kharagpur13, French Institute for Research in Computer Science and Automation14, University of New Mexico15, Charles University in Prague16, Birla Institute of Technology and Science17, Indian Institute of Technology Bombay18, University of West Bohemia19
TL;DR: The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem.
Abstract: SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.
1,126 citations
TL;DR: In this paper, the continuous-time quantum Monte Carlo (QMC) algorithm is used to solve the local correlation problem in quantum impurity models with high and low energy scales and is effective for wide classes of physically realistic models.
Abstract: Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self energy and local correlation functions. These applications require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms reviewed in this article meet this challenge. We present derivations and descriptions of the algorithms in enough detail to allow other workers to write their own implementations, discuss the strengths and weaknesses of the methods, summarize the problems to which the new methods have been successfully applied and outline prospects for future applications.
1,116 citations
TL;DR: These studies suggest that Pluronics have a broad spectrum of biological response modifying activities which make it one of the most potent drug targeting systems available, resulting in a remarkable impact on the emergent field of nanomedicine.
1,111 citations
University of Copenhagen1, University of Gothenburg2, Technical University of Denmark3, Leiden University4, Lund University5, University of Oxford6, University of Wrocław7, University of Zurich8, Wrocław Medical University9, University of Toronto10, Gorno-Altaisk State University11, South Ural State University12, Polish Academy of Sciences13, Ludwig Maximilian University of Munich14, Hungarian Natural History Museum15, Eötvös Loránd University16, Hungarian Academy of Sciences17, Masaryk University18, Academy of Sciences of the Czech Republic19, University of Tartu20, Yerevan State University21, Hungarian National Museum22, University of Szeged23, University of Wisconsin-Madison24, Russian Academy of Sciences25, First Faculty of Medicine, Charles University in Prague26, Armenian National Academy of Sciences27, Moscow State University28, University of California, Berkeley29
TL;DR: It is shown that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia.
Abstract: The Bronze Age of Eurasia (around 3000-1000 BC) was a period of major cultural changes. However, there is debate about whether these changes resulted from the circulation of ideas or from human migrations, potentially also facilitating the spread of languages and certain phenotypic traits. We investigated this by using new, improved methods to sequence low-coverage genomes from 101 ancient humans from across Eurasia. We show that the Bronze Age was a highly dynamic period involving large-scale population migrations and replacements, responsible for shaping major parts of present-day demographic structure in both Europe and Asia. Our findings are consistent with the hypothesized spread of Indo-European languages during the Early Bronze Age. We also demonstrate that light skin pigmentation in Europeans was already present at high frequency in the Bronze Age, but not lactose tolerance, indicating a more recent onset of positive selection on lactose tolerance than previously thought.
1,088 citations
TL;DR: In this article, the design and structure of coordination polymers derived from Ag(I) with N-donor ligands and their role in the investigation of weak non-covalent interactions in the solid state are discussed.
1,085 citations
Authors
Showing all 68238 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Krzysztof Matyjaszewski | 169 | 1431 | 128585 |
| A. Gomes | 150 | 1862 | 113951 |
| Robert J. Sternberg | 149 | 1066 | 89193 |
| James M. Tour | 143 | 859 | 91364 |
| Alexander Belyaev | 142 | 1895 | 100796 |
| Rainer Wallny | 141 | 1661 | 105387 |
| I. V. Gorelov | 139 | 1916 | 103133 |
| António Amorim | 136 | 1477 | 96519 |
| Halina Abramowicz | 134 | 1192 | 89294 |
| Grigory Safronov | 133 | 1358 | 94610 |
| Elizaveta Shabalina | 133 | 1421 | 92273 |
| Alexander Zhokin | 132 | 1323 | 86842 |
| Eric Conte | 132 | 1206 | 84593 |
| Igor V. Moskalenko | 132 | 542 | 58182 |
| M. Davier | 132 | 1449 | 107642 |