Molecular Dynamic

Molecular dynamics (MD) simulations model the movement of atoms and molecules to understand material properties and chemical processes, but are computationally expensive. Current research focuses on accelerating MD through machine learning (ML) force fields, often employing graph neural networks (GNNs) and other deep learning architectures to predict interatomic forces and energies more efficiently than traditional methods. These advancements enable faster and more accurate simulations of complex systems, impacting fields like drug discovery, materials science, and the study of biological processes by providing insights into reaction mechanisms, conformational changes, and thermodynamic properties. Furthermore, active learning and enhanced sampling techniques are being developed to improve the efficiency and accuracy of these ML-driven simulations.