Quantum Chemical Property

Quantum chemical property prediction aims to rapidly and accurately compute molecular properties using machine learning (ML), reducing the computational cost of traditional quantum chemistry methods. Current research focuses on developing and improving multifidelity ML models, which leverage data from various levels of quantum chemical accuracy, and employing advanced graph neural network architectures like message-passing networks and equivariant networks to better capture molecular structure and interactions. These advancements enable faster and more accurate predictions of diverse properties, impacting materials discovery, drug design, and other fields requiring detailed molecular understanding.