Quantum Chemistry

Quantum chemistry aims to computationally predict molecular properties using quantum mechanics, traditionally relying on computationally expensive methods like Density Functional Theory (DFT) and Coupled Cluster. Current research heavily utilizes machine learning, employing graph neural networks, transformers, and restricted Boltzmann machines to create faster and more accurate predictive models, often trained on large datasets of molecular geometries and properties calculated at various levels of theory (multifidelity methods). These advancements significantly accelerate simulations, enabling studies of larger and more complex molecules, with applications ranging from drug discovery to materials science.