Inference of Molecular Interaction Potential With Machine Learning

Student: Veprentsev Ivan

Supervisor: Nikita Kazeev

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2021

There are numerous existing methods describing different chemical processes, that often require the use of computationally demanding quantum methods such as DFT(density-functional theory) etc. The main drawback of this approach is the unfeasibility of long simulations for large atomic systems. A few new machine-learning methods have been developed recently, that can solve this problem and approximate atomic motions. However, they also have several drawbacks and molecular dynamics methods still have lots of room for research. In this work we try to implement meta-model to make the process of learning faster for new systems.

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