Molecular System

Molecular system modeling aims to accurately and efficiently simulate the behavior of molecules and their interactions, crucial for advancing fields like drug discovery and materials science. Current research heavily utilizes machine learning, employing graph neural networks (GNNs), normalizing flows, and deep reinforcement learning to predict properties like energy, forces, and transition pathways, often incorporating physics-informed biases for improved accuracy and efficiency. These advancements enable faster and more accurate simulations of complex systems, overcoming limitations of traditional methods and facilitating the design of novel molecules and materials with desired properties.