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Titlebook: Optimization in Computational Chemistry and Molecular Biology; Local and Global App C. A. Floudas,P. M. Pardalos Book 2000 Springer Science

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書目名稱Optimization in Computational Chemistry and Molecular Biology
副標(biāo)題Local and Global App
編輯C. A. Floudas,P. M. Pardalos
視頻videohttp://file.papertrans.cn/704/703245/703245.mp4
叢書名稱Nonconvex Optimization and Its Applications
圖書封面Titlebook: Optimization in Computational Chemistry and Molecular Biology; Local and Global App C. A. Floudas,P. M. Pardalos Book 2000 Springer Science
描述.Optimization in Computational Chemistry and MolecularBiology:. .Local and Global Approaches. covers recentdevelopments in optimization techniques for addressing severalcomputational chemistry and biology problems. A tantalizing problemthat cuts across the fields of computational chemistry, biology,medicine, engineering and applied mathematics is how proteins fold.Global and local optimization provide a systematic framework ofconformational searches for the prediction of three-dimensionalprotein structures that represent the global minimum free energy, aswell as low-energy biomolecular conformations. .Each contribution in the book is essentially expository in nature, butof scholarly treatment. The topics covered include advances in localand global optimization approaches for molecular dynamics andmodeling, distance geometry, protein folding, molecular structurerefinement, protein and drug design, and molecular and peptidedocking. ..Audience:. The book is addressed not only to researchers inmathematical programming, but to all scientists in various disciplineswho use optimization methods in solving problems in computationalchemistry and biology.
出版日期Book 2000
關(guān)鍵詞algorithm; algorithms; database; databases; global optimization; linear optimization; local optimization; m
版次1
doihttps://doi.org/10.1007/978-1-4757-3218-4
isbn_softcover978-1-4419-4826-7
isbn_ebook978-1-4757-3218-4Series ISSN 1571-568X
issn_series 1571-568X
copyrightSpringer Science+Business Media Dordrecht 2000
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Optimization of Carbon and Silicon Cluster Geometry for Tersoff Potential using Differential Evoluth. The algorithm is tested on hard practical problems from computational chemistry. These are the problems of semi-empirical many-body potential energy functions considered for carbon-carbon and silicon-silicon atomic interactions. The minimum binding energies of both carbon and silicon clusters consisting of upto 15 particles are reported.
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