ML in the Theoretical Analysis of Molecular Dynamics
In recent years, the use of machine learning (ML) methods has become increasingly important in the field of (theoretical) analysis of molecular systems. In this context, ML supports both the data evaluation of experiments and the data generation using various simulation methods from the quantum chemical to the macroscopic scale. In the field of density functional theory and molecular dynamics simulations, ML models already outperform classical methods in terms of accuracy and simulation time. At the same time, ML models do not always supersede classical methods, but rather extend them and provide new approaches.
In the field of data evaluation, cluster algorithms and regression methods enable the recognition of highly complex correlations in high-dimensional spaces. Image recognition software can simplify or take over the evaluation of spectra. Ultimately, highly automated processes can be created that require minimal human intervention, enabling high-throughput experiments in which a computer uses Active Learning to decide which experiments to perform next.
In the coming years, we can expect to see an increasing number of applications and further exploration of the possibilities of machine learning in the field of molecular systems analysis.