Quantum Chemical Calculation

Quantum chemical calculations, which determine the properties of molecules using quantum mechanics, are computationally expensive, limiting their application. Current research focuses on accelerating these calculations through machine learning, employing architectures like graph neural networks and tensor networks to create accurate and efficient potential energy surfaces. These machine learning potentials are trained on large datasets of quantum chemical calculations, often using active learning strategies to optimize data collection and improve model robustness. This work significantly impacts fields like drug discovery and materials science by enabling faster and more extensive simulations of molecular systems.