Force Field

Force fields are mathematical models representing the potential energy of molecular systems, crucial for simulating their behavior in molecular dynamics. Current research heavily emphasizes machine learning (ML) approaches, employing graph neural networks (GNNs), equivariant neural networks, and transformer architectures to create accurate and efficient force fields, often incorporating physical priors or data fusion strategies to improve generalizability and stability. These advancements are significantly impacting various fields, enabling more accurate and computationally feasible simulations of complex systems in materials science, drug discovery, and biophysics. The development of robust and transferable ML force fields is a key focus, addressing challenges in data efficiency and model generalizability across diverse chemical spaces.