Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network approach
| العنوان: | Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network approach |
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| المؤلفون: | F S Carvalho, João P. Braga |
| المصدر: | Journal of Molecular Modeling. 26 |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Physics, 010304 chemical physics, Artificial neural network, Organic Chemistry, Mathematical analysis, Monte Carlo method, 010402 general chemistry, Radial distribution function, 01 natural sciences, Catalysis, Ideal gas, 0104 chemical sciences, Computer Science Applications, Inorganic Chemistry, symbols.namesake, Fourier transform, Computational Theory and Mathematics, Step function, 0103 physical sciences, symbols, Finite potential well, Physical and Theoretical Chemistry, Structure factor |
| الوصف: | Hopfield neural network was used to retrieve liquid gallium radial distribution function from an experimental structure factor, obtained at 959 K. The inversion framework was carried out under two initial conditions: (a) a constant radial distribution function corresponding to an ideal gas and (b) a step function, simulating a gas with square well potential of interaction. Both situations lead to accurate inverse results if compared with the radial distribution function obtained by Bellisent-Funel et al., using the Fourier transform method and Monte Carlo simulation. The Hopfield neural network has shown to be a powerful strategy to calculate the radial distribution function from experimental data. |
| تدمد: | 0948-5023 1610-2940 |
| DOI: | 10.1007/s00894-020-04436-y |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::557dfb33bd05c0cb34c6c7fa63c7d4de https://doi.org/10.1007/s00894-020-04436-y |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....557dfb33bd05c0cb34c6c7fa63c7d4de |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 09485023 16102940 |
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| DOI: | 10.1007/s00894-020-04436-y |