6. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
6
7. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
7
8. タイトル:
“Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and
Machine Learning”
出展:
Journal of the American Chemical Society,Impact Factor:14.357
論文1
Wakasugi, Panasonic Corp.
8
25万個の数値計算データを用いて,ペロブスカイト系材料の熱力学的安定性を予測
A,B,Xの各配置に各元素を置いたとき
の安定性を予測.
入力:Pymatgenで作成(119次元)
→重要度解析し,11個を選択
3元素で合計33次元.
出力:安定性
データ:25万個の計算データ
Xに各元素を置いたときの安定構造の数を色で表現
10. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
10
11. タイトル:
“Predicting thermoelectric properties from crystal graphs and material descriptors - first
application for functional materials”
出展:
NIPS 2018 Workshop
論文2
Wakasugi, Panasonic Corp.
11
Data Augmentationした約300万個の数値計算データからPower Factorを予測
従来(計算科学)
課題:計算コストが高い
提案法(深層学習)
入力にDFT記述子(低精度,低計算コスト)を利用
DFT記述子の代わりにCGCNN(結晶性を考慮したCNN)を利用
13. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
13
14. タイトル:“A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
出展: Scientific Reports,Impact Factor:4.122
論文3
Wakasugi, Panasonic Corp.
14
波数空間で表現した電子密度に対して3DCNNを適用し,エネルギーを予測
電気的,熱的特性に関与する電子密度を波数空間で表現
3DCNNによって特徴抽出し,エネルギーを予測
16. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
16
17. タイトル:“Powerful, transferable representations for molecules through intelligent task selection in
deep multitask networks”
出展:NIPS 2018 Workshop
論文4
Wakasugi, Panasonic Corp.
17
タスク間の関連度をスコア化し,サポートタスクを自動抽出.
有機分子の特性予測タスクにおいて,関連タスクを自動抽出
サポートタスクとして転移学習することで性能を向上
19. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
19
22. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
(NIPS 2018 Workshop)
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
(NIPS 2018 Workshop)
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
紹介論文
Wakasugi, Panasonic Corp.
22
23. タイトル:“Representations in neural network based empirical potentials”
出展:The Journal of Chemical Physics, Impact Factor:2.843
論文6
Wakasugi, Panasonic Corp.
23
Si結晶系のエネルギー予測において,NNにおける情報処理過程を解析
様々なSi配位に対し,対
称関数で特徴量化.
NNによってエネルギーを
予測.
25. 1. “Predicting the Thermodynamic Stability of Solids Combining Density
Functional Theory and Machine Learning”
25万個の数値計算データを用いて,ペロブスカイト系材料の熱力学的安定性を予測
2. “Predicting thermoelectric properties from crystal graphs and material
descriptors - first application for functional materials”
Data Augmentationした約300万個の数値計算データからPower Factorを予測
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep
Convolutional Neural Networks”
波数空間で表現した電子密度に対して3DCNNを適用し,エネルギーを予測
4. “Powerful, transferable representations for molecules through intelligent task
selection in deep multitask networks”
タスク間の関連度をスコア化し,サポートタスクを自動抽出.
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
Conditional VAE,Conditional GANにより,所望の特性を持つ材料を生成
6. “Representations in neural network based empirical potentials”
Si結晶系のエネルギー予測において,NNにおける情報処理過程を解析
論文概要まとめ
Wakasugi, Panasonic Corp.
25
28. 1. “Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning”
(Journal of the American Chemical Society,Impact Factor:14.357)
https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.7b00156
2. “Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional
materials”
(NIPS 2018 Workshop)
https://arxiv.org/abs/1811.06219
3. “A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks”
(Scientific Reports,Impact Factor:4.122)
https://www.nature.com/articles/s41598-017-17299-w
4. “Powerful, transferable representations for molecules through intelligent task selection in deep multitask
networks”
(NIPS 2018 Workshop)
https://arxiv.org/abs/1809.06334
5. “Incomplete Conditional Density Estimation for Fast Materials Discovery”
(NIPS 2018 Workshop)
https://truyentran.github.io/papers/incomplete-alloy.pdf
6. “Representations in neural network based empirical potentials”
(The Journal of Chemical Physics, Impact Factor:2.843)
https://aip.scitation.org/doi/10.1063/1.4990503
紹介論文
Wakasugi, Panasonic Corp.
28