Open Source UN-Conference 2024 Kawagoe - 独自OS「DaisyOS GB」の紹介
[DL輪読会]Inverse Design of Solid-State Materials via a Continuous Representation
1. 1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
“Inverse Design of Solid-State Materials via a Continuous
Representation”
Kensuke Wakasugi, Panasonic Corporation.
2. タイトル:
Inverse Design of Solid-State Materials via a Continuous Representation [1]
(Cellに属するMatterというジャーナル、昨年できたらしい)
著者:
Juhwan Noh 1, Jaehoon Kim 2, Helge S. Stein 3, Benjamin Sanchez-Lengeling 4,
John M. Gregoire 3, Alan Aspuru-Guzik 567, Yousung Jung 128,
選書理由:
計算科学の分野における構造生成に興味があったため.
※特に断りがない限り,図・表・式は上記論文より引用したものです.
書誌情報
Wakasugi, Panasonic Corp.
2
1 Department of Chemical and Biomolecular Engineering,KoreaAdvancedInstituteofScience and Technology (KAIST), 291 Daehakro, Daejeon 34141, Korea
2 Graduate School of EEWS, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehakro, Daejeon 34141, Korea
3 Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125, USA
4 DepartmentofChemistry and ChemicalBiology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA
5 Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
6 Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
7 Canadian Institute for Advanced Research (CIFAR) Senior Fellow, Toronto, ON M5S 1M1, Canada
[1] Noh, J., Kim, J., Stein, H. S., Sanchez-Lengeling, B., Gregoire, J. M., Aspuru-Guzik, A., & Jung, Y. (2019). Inverse Design of Solid-
State Materials via a Continuous Representation. Matter, 1(5), 1370-1384.
19. 関連研究
Wakasugi, Panasonic Corp.
19
Crystal Structure Prediction via Deep Learning(2018) [2]
[2]より引用
[2] Ryan, K., Lengyel, J., & Shatruk, M. (2018). Crystal structure prediction via deep learning. Journal of the American Chemical
Society, 140(32), 10158-10168.
[1]より引用
単位構造中の原子周りの他の原子の配置(距離)について
窓関数をかけたうえで,回転方向で積算
三次元情報の欠落を防ぐため,摂動を加え(Multiple)12個のPerspectiveを生成
12個のPerspectiveを画像としてVAEに入力
20. 関連研究
Wakasugi, Panasonic Corp.
20
Crystal Structure Prediction via Deep Learning(2018) [2]
[2] Ryan, K., Lengyel, J., & Shatruk, M. (2018). Crystal structure prediction via deep learning. Journal of the American Chemical
Society, 140(32), 10158-10168.
単位構造の原子一つに対し,再構成を試みる
→全原子についての確率の積が構造のもっと
もらしさ
既存の構造に対し,新たな元素配置を検討し
新規構造発見に用いる
[2]より引用
21. 関連研究
Wakasugi, Panasonic Corp.
21
Deep-learning approach to the structure of amorphous silicon(2019) [3]
[3] Comin, M., & Lewis, L. J. (2019). Deep-learning approach to the structure of amorphous silicon. Physical Review B, 100(9), 094107.
大きめの単位構造を考え(原子216個)、
原子座標に対する生成モデルを構築
エネルギー的に安定なランダムな配置を学習.
低コストでSiのシミュレーションを実現できる
[3]より引用
22. 関連研究
Wakasugi, Panasonic Corp.
22
Deep-learning approach to the structure of amorphous silicon(2019) [3]
[3] Comin, M., & Lewis, L. J. (2019). Deep-learning approach to the structure of amorphous silicon. Physical Review B, 100(9), 094107.
学習曲線と、生成例
エネルギー的に安定な原子配置を生成する
[3]より引用
23. 生成対象の比較
[1] Noh, J., Kim, J., Stein, H. S., Sanchez-Lengeling, B., Gregoire, J. M., Aspuru-Guzik, A., & Jung, Y. (2019).
Inverse Design of Solid-State Materials via a Continuous Representation. Matter, 1(5), 1370-1384.
→構造を連続空間で定義し,構造を生成
[2] Ryan, K., Lengyel, J., & Shatruk, M. (2018). Crystal structure prediction via deep learning. Journal of the
American Chemical Society, 140(32), 10158-10168.
→原子座標の生成は行わず,既存の構造をDBとして新規の原子配置を生成
[3] Comin, M., & Lewis, L. J. (2019). Deep-learning approach to the structure of amorphous silicon.
Physical Review B, 100(9), 094107.
→対象の系を限定したうえで,DFTまたはMD計算を機械学習で代替し,原子座標を生成
Wakasugi, Panasonic Corp.
23
25. 文献情報
[1] Noh, J., Kim, J., Stein, H. S., Sanchez-Lengeling, B., Gregoire, J. M., Aspuru-Guzik, A., & Jung, Y. (2019).
Inverse Design of Solid-State Materials via a Continuous Representation. Matter, 1(5), 1370-1384.
[2] Ryan, K., Lengyel, J., & Shatruk, M. (2018). Crystal structure prediction via deep learning. Journal of the
American Chemical Society, 140(32), 10158-10168.
[3] Comin, M., & Lewis, L. J. (2019). Deep-learning approach to the structure of amorphous silicon.
Physical Review B, 100(9), 094107.
[4] Agrawal, A., & Choudhary, A. (2019). Deep materials informatics: Applications of deep learning in
materials science. MRS Communications, 9(3), 779-792.
[5] Schmidt, J., Marques, M. R., Botti, S., & Marques, M. A. (2019). Recent advances and applications of
machine learning in solid-state materials science. npj Computational Materials, 5(1), 1-36
Wakasugi, Panasonic Corp.
25