PHAM TIEN LAM
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Ph.D. (Computational Materials Science, Japan Advanced Institute of Science and Technology, Japan, 2011)
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M.Sc. (Computational Materials Science, Hanoi University of Science, VNU-Hanoi, 2006)
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B.Sc. (Computational Materials Science, Hanoi National University of Education, 2004)
Assoc. Prof. Dr. Pham Tien Lam is a distinguished researcher and Lecturer at Phenikaa University, with deep expertise in computational materials science and AI-based materials informatics. He has held multiple research appointments in Japan, including at the Japan Advanced Institute of Science and Technology and the Institute for Solid State Physics, University of Tokyo. His academic work spans defect energetics, orbital interactions, and neural network models for materials property prediction.
Dr. Lam is the Principal Investigator for several national and international AI-focused materials projects funded by NAFOSTED and VKIST. He also serves as an AI consultant for smart technology enterprises and previously worked as a big data consultant for the World Bank. He has authored more than 15 publications in high-impact journals including IUCrJ, Cell Reports Physical Science, and Computational Materials Science.
RESEARCH INTERESTS
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Computational Materials Science and Defect Modeling
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Deep Learning for Materials Discovery
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Machine Learning for Atomic Forces and Structure Prediction
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AI in Scientific Discovery and Simulation Systems
SELECTED PUBLICATIONS
- Van-Quyen Nguyen, Viet-Cuong Nguyen, Tien-Cuong Nguyen, Nguyen-Xuan-Vu Nguyen, Tien-Lam Pham “Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network”, (2022), Com. Mat, 209, 111379
- Tien-Cuong Nguyen, Van-Quyen Nguyen, Van-Linh Ngo, Quang-Khoat Than, Tien-Lam Pham, “Learning hidden chemistry with deep neural network”, (2021) Com. Mat. 200, 110784.
- Tien-Lam Pham, Duong-Nguyen Nguyen, Minh-Quyet Ha, Hiori Kino, Takashi Miyatake, Hieu-Chi Dam, “Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship”, (2020) IUCrJ
- Pham Tien Lam, Nguyen Van Duy, Nguyen Tien Cuong, “Machine Learning Representation for Atomic Forces and Energies”, (2020) VNU Journal of Science: Mathematics-Physics
- Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam “Ensemble learning reveals dissimilarity between rare-earth transition binary alloys with respect to the Curie temperature”, (2019) J. Phys. Mater. 2 034009
- Van-Doan Nguyen, Tien-Lam Pham, Hieu-Chi Dam, “Application of materials informatics on crystalline materials for two-body terms approximation”, 2019, Computational Materials Science 166, 155-161
- Tien-Lam Pham, Tran-Thai Dang, Van-Doan Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam, “Learning Materials Properties from Orbital Interactions”, (2019) Journal of Physics: Conference Series 1290 (1), 012012.
- Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Anh-Tuan Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam, “A regression-based model evaluation of the Curie temperature of transition-metal rare-earth compounds”, (2019) Journal of Physics: Conference Series 1290 (1), 012009
- Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, DAM Hieu-Chi, “Ensemble learning reveals dissimilarity between rare-earth transition binary alloys with respect to the Curie temperature“, (2019), Phys. Mater. in press. https://doi.org/10.1088/2515-7639/ab1738
- Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, K Takahashi, Hieu-Chi Dam, “Committee machine that votes for similarity between materials“, (2018), IUCrJ 5 (6).
PROFESSIONAL CERTIFICATIONS
- AWS Certified Instructor
- NVIDIA DLI Certified Instructor
COURSES TAUGHT
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CSE703020 - Machine Learning
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EEE703035 – Deep Learning
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CSE703065 – Natural Language Processing
15 Floor, A9 Building
Phenikaa University
Nguyen Van Trac, Yen Nghia
Ha Noi, Viet Nam