Relaxed Energy

Relaxed energy prediction, crucial for materials discovery and design, focuses on accurately calculating the minimum energy state of a molecular system after structural optimization. Current research employs machine learning models, particularly graph neural networks (GNNs) and transformers, to predict relaxed energies faster and more efficiently than traditional methods like density functional theory (DFT). These models are being refined to improve accuracy and uncertainty quantification, addressing challenges like incorporating 3D interactions and handling incomplete structural information. The ability to rapidly and reliably predict relaxed energies accelerates materials discovery, enabling the efficient screening of potential candidates for various applications.