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pro vyhledávání: '"Goscinski, Alexander"'
The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demandi
Externí odkaz:
http://arxiv.org/abs/2310.12579
Publikováno v:
The Journal of Chemical Physics 155(10), 104106 (2021)
The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular represent
Externí odkaz:
http://arxiv.org/abs/2105.08717
Autor:
Musil, Félix, Veit, Max, Goscinski, Alexander, Fraux, Guillaume, Willatt, Michael J., Stricker, Markus, Junge, Till, Ceriotti, Michele
Physically-motivated and mathematically robust atom-centred representations of molecular structures are key to the success of modern atomistic machine learning (ML) methods. They lie at the foundation of a wide range of methods to predict the propert
Externí odkaz:
http://arxiv.org/abs/2101.08814
Eficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact tha
Externí odkaz:
http://arxiv.org/abs/2009.02741
Akademický článek
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Autor:
Goscinski, Alexander
A sequential application of the Grover algorithm to solve the iterated search problem has been improved by Ozhigov by parallelizing the application of the oracle. In this work a representation of the parallel Grover as dynamic system of inversion abo
Externí odkaz:
http://arxiv.org/abs/1808.03347
Akademický článek
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Autor:
Goscinski A; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Principe VP; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Fraux G; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Kliavinek S; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Helfrecht BA; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland.; Pacific Northwest National Laboratory, Richland, WA, 99352, USA., Loche P; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Ceriotti M; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland., Cersonsky RK; Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland.; Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA.
Publikováno v:
Open research Europe [Open Res Eur] 2023 Sep 18; Vol. 3, pp. 81. Date of Electronic Publication: 2023 Sep 18 (Print Publication: 2023).
