Artificial intelligence is now widely used, including in industry, from logical exploration in the 1990s to deep learning in the 2000s, supported by advances in hardware, and research has shown significant progress.  The application has been widely advanced in a wide range of fields, including pattern recognition (voice and image), game entertainment, natural language processing, and data analysis. Even at our university, there is a strong relationship with the intelligent robotics field, human life design field, and game entertainment field. At present, the research on artificial intelligence expands the impact of the increasing use of applications on society, and ensuring the reliability of artificial intelligence technology has become an important issue.


The aim of this center is to return to the basics of machine learning technology such as deep learning, and aim to secure empirical reliability as well as critical application of interpretable and explainable judgment by artificial intelligence. The center also aims to be an interface for joint research with overseas.


Professor Nguyen Minh Le (Director of the center, IS School)

Professor Satoshi Tojo (IS School)

Professor Mizuhito Ogawa (IS School)

Professor Hashimoto Takashi (KS School)

Professor Huynh Van Nam (KS School)

Professor Dam Hieu Chi (KS School)

Associate Professor Shogo Okada (IS School)


1. Solvent Screening for Efficient Chemical Exfoliation of Graphite, Nhan Nu Thanh Ton, Minh-Quyet Ha, Takuma Ikenaga, Ashutosh Thakur, Hieu-Chi Dam*, Toshiaki Taniike*, 2D Materials, 2021, 8, 015019. IF=6.88,
2. Maximum Separated Distribution with High Interpretability Found Using an Exhaustive Search Method—Application to Magnetocrystalline Anisotropy of Fe/Co Films—, Hiori Kino*, Kohji Nakamura, Koji Hukushima, Takashi Miyake, Dam Hieu Chi*, Journal of the Physical Society of Japan, 2020, 89 (6), 064802. IF=1.579
3. Boron cage effects on Nd–Fe–B crystal structure’s stability, Duong-Nguyen Nguyen*, Duc-Anh Dao, Takashi Miyake, Hieu-Chi Dam, The Journal of Chemical Physics, 2020, 153, 11, 114111. IF=2.991
4. Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship, Tien-Lam Pham, Duong-Nguyen Nguyen, Minh-Quyet Ha, Hiori Kino, Takashi Miyake, Hieu-Chi Dam*, IUCrJ, 2020, 7, 6, pp. 1036-1047. IF=5.401
5. Characterization of descriptors in machine learning for data-based sputtering yield prediction, Hiori Kino, Kazumasa Ikuse, Hieu-Chi Dam, Satoshi Hamaguchi, Physics of Plasmas, 2021, 28, 1, 013504. IF=1.913,

6. Anh-Tu Tran, The-Dung Luong, Jessada Karnjana, Van-Nam Huynh: An efficient approach for privacy preserving decentralized deep learning models based on secure multi-party computation. Neurocomputing 422: 245-262 (2021)
7. Duc-Vinh Vo, Jessada Karnjana, Van-Nam Huynh: An integrated framework of learning and evidential reasoning for user profiling using short texts. Inf. Fusion 70: 27-42 (2021)
8. Nguyen-Duy Hung, Van-Nam Huynh: Revealed preference in argumentation: Algorithms and applications.
International Journal of Approximate Reasoning 131, 214-251 (2021).

9.May Myo Zin, Teeradaj Racharak, Nguyen Minh Le: Construct-Extract: An Effective Model for Building Bilingual Corpus to Improve English-Myanmar Machine Translation. ICAART (2) 2021: 333-342
10. Phi Manh Kien, Ha-Thanh Nguyen, Ngo Xuan Bach, Vu Tran, Minh Le Nguyen, Tu Minh Phuong:
Answering Legal Questions by Learning Neural Attentive Text Representation. COLING 2020: 988-998
11. Tung Le, Huy Tien Nguyen, Nguyen Le Minh: Integrating Transformer into Global and Residual Image Feature Extractor in Visual Question Answering for Blind People. KSE 2020: 31-36
12. VK Tran, Nguyen Le Minh, Variational model for low-resource natural language generation in spoken dialogue systems, Computer Speech & Language 65, 101120 (citation:01)
13. ReLink: Open Information Extraction by Linking Phrases and Its ApplicationsXC Tran, LM Nguyen International Conference on Distributed Computing and Internet Technology, 44-62
We integrate machine learning techniques with explainability for applications in materials discovery.