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Titlebook: Methods in Computational Chemistry; Volume 1 Electron Co Stephen Wilson Book 1987 Springer Science+Business Media New York 1987 chemistry.c

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發(fā)表于 2025-3-21 16:57:00 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Methods in Computational Chemistry
副標題Volume 1 Electron Co
編輯Stephen Wilson
視頻videohttp://file.papertrans.cn/633/632288/632288.mp4
圖書封面Titlebook: Methods in Computational Chemistry; Volume 1 Electron Co Stephen Wilson Book 1987 Springer Science+Business Media New York 1987 chemistry.c
描述When, forty years ago, as a student of Charles Coulson in Oxford I began work in theoretical chemistry, I was provided with a Brunsviga calculator-a small mechanical device with a handle for propulsion, metal levers for setting the numbers, and a bell that rang to indicate overflow. What has since come to be known as computational chemistry was just beginning. There followed a long period in which the fundamental theory of the "golden age" (1925-1935) was extended and refined and in which the dreams of the early practitioners were gradually turned into hard arithmetic reality. As a still-computing survivor from the early postwar days now enjoying the benefits of unbelievably improved hardware, I am glad to contribute a foreword to this series and to have the opportunity of providing a little historical perspective. After the Brunsviga came the electromechanical machines of the late 1940s and early 1950s, and a great reduction in the burden of calculating molecular wavefunctions. We were now happy. At least for systems con- taining a few electrons it was possible to make fully ab initio calculations, even though semiempirical models remained indispensable for most molecules of every
出版日期Book 1987
關鍵詞chemistry; computational chemistry; electrons; molecule; theoretical chemistry
版次1
doihttps://doi.org/10.1007/978-1-4899-1983-0
isbn_softcover978-1-4899-1985-4
isbn_ebook978-1-4899-1983-0
copyrightSpringer Science+Business Media New York 1987
The information of publication is updating

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S. Wilson. To well monitor and optimize battery manufacturing process, it is vital to design a data-driven approach for effectively modelling and classifying the product properties within this complicated production chain. In this paper, a support vector machine (SVM)-based framework, through using four vari
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