Nowadays, discovering new materials is very important because each technology is related to a certain material/group of materials. Many urgent issues such as clean and sustainable energy sources, reducing greenhouse gas emissions that increase the earth’s temperature, etc. can be solved if suitable materials are synthesized. Quantum technology, with quantum computers and quantum information, is believed to be able to create fundamental changes, which will also become a reality when materials with certain properties are discovered. Exploring new materials experimentally is limited by costs and time. Current urgent issues of clean energy development, new generation semiconductor chips, quantum technology, etc. pose demands for acceleration in finding/synthesizing materials with precise determined properties to meet those needs. Computation, along with theory and experiment, has become one of the three pillars of science today. In the field of materials science, calculations based on density functional theory (DFT)—in which high-performance computers are used to solve quantum equations describing electrons in materials—have become an important tool, very useful when combined with experiments to research and discover new materials. Recently, approaches based on big data and machine learning methods have significantly improved the efficiency of the DFT method, allowing the simulation of material systems with larger sizes and longer times.
The project “Prediction, characterization and design of advanced materials by machine-learning assisted density-functional based simulations” proposes a new approach in this direction with the goal:
– Develop more effective methods for computational materials science research appropriate to the limited computational resources in Vietnam;
– Conduct quality research on materials with potential applications in important technologies such as energy and quantum technology;
– Build a strong research team with members from many research institutes and universities, aiming to cooperate with domestic experimental research groups.
Main tasks of the project:
– Build models for twisted layered materials based on machine learning methods.
– Develop a heat transfer simulation method at the atomic level from the built machine learning model.
– Research the thermoelectric performance of materials under the influence of doping and deformation.
– Research new quantum states in 2-D materials, especially states in which electronic correlation plays an important role.
