[Published online Journal of Computer Chemistry, Japan Vol.18, 150-151, by J-STAGE]
<Title:> マシンラーニングによる配座解析の時間短縮
<Author(s):> 﨑山 博史
<Corresponding author E-Mill:> saki(at)sci.kj.yamagata-u.ac.jp
<Abstract:> In the conformational analysis of [Mg(dmso)6]2+ complex cation (dmso: dimethylsulfoxide), 130 candidates of the conformers were successfully narrowed down to 26 conformers by machine learning. As a result, the time required for the structural optimization turned out to be reduced to 1/8, and the machine learning was found to be effective in timesaving for conformational analysis.
<Keywords:> マシンラーニング, 配座解析, 正八面体型錯体, ディープニューラルネットワーク, 構造予測
<URL:> https://www.jstage.jst.go.jp/article/jccj/18/3/18_2019-0020/_article/-char/ja/
