VINIF.2019.DA03 – Novel functional materials discovery by machine-learning approaches

project manager image
Principle Investigator
Prof. Vu Ngoc Tuoc
Host Organization
Isntitute of Engineering Physics, Hanoi University of Science and Technology

The project Novel functional materials discovery by machine-learning approaches introduces a new interdisciplinary research field – Materials Informatics (MI) – a new direction of Materials Science and Technology based on Artificial Intelligence and Big Data machine learning using machine learning algorithms, to the Vietnamese scientific community.

MI is a strongly interdisciplinary research field that requires the coordination of experts in physics, chemistry, materials science and information technology to solve a real problem: new materials development for technology applications in a better, faster and cheaper way. Machine learning and artificial intelligence tools based on large databases are growing rapidly, dominating the activities of most areas of life and are a solid basis for this goal. From our perspective, Vietnam has several relative advantages to participate in this scientific trend, which are (1) researchers who have been well trained in the past decade and can immediately participate in this field, (2) there are many Vietnamese experts in the world willing to cooperate with colleagues from Vietnam and (3) the financial needs for research in this area are affordable and the computational resources needed can be arranged with considerable flexibility. [Left image taken from A. Agrawal et al. “Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science,” Appl. Phys. Lett., vol. 4, p. 053208, 2016.]

project manager image
Principle Investigator
Prof. Vu Ngoc Tuoc
Host Organization
Isntitute of Engineering Physics, Hanoi University of Science and Technology

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Expect Progress
03/09/2019
03/04/2020
Phase 1

– One Q1 journal article

03/01/2021
Phase 2

– 02 Q1 journal article

03/09/2021
Phase 3

– 02 Q1 journal article
– Open database set

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