@article{oai:sangitan.repo.nii.ac.jp:00000018,
 author = {森, 英喜 and 奥村, 雅彦 and 板倉, 充洋},
 issue = {１},
 journal = {産業技術短期大学誌, BULLETIN OF COLLEGE OF INDUSTRIAL TECHNOLOGY},
 month = {Mar},
 note = {The inter-atomic potential based on artificial neural network (ANN) is very promising tool for atomic modeling. Using high quality training set constructed by First principles calculation based on Density functional theory (DFT), ANN potential would become sophisticated replica of DFT. However, the transferability of ANN potential depends on the DFT training dataset. In this study, we perform molecular dynamics (MD) calculations for molten iron, which is not directly included in the training data. We also examine the results to discuss the applicability of the constructed potential.},
 pages = {49--51},
 title = {08_機械学習ポテンシャルによる溶融鉄構造再現性の検討},
 volume = {55},
 year = {2022},
 yomi = {モリ, ヒデキ and オクムラ, マサヒコ and イタクラ, ミツヒロ}
}