Ab Initio Quantum Chemistry

Ab initio quantum chemistry aims to solve the Schrödinger equation for molecules from first principles, without relying on empirical parameters. Current research heavily emphasizes the development and application of neural network-based methods, including variational Monte Carlo with novel architectures like transformers and orbital-free density functional theory enhanced by deep learning, to improve accuracy and scalability for larger molecular systems. These advancements are driven by the need for more efficient and accurate calculations of molecular properties, impacting fields ranging from materials science to drug discovery. The focus is on overcoming computational bottlenecks through innovative algorithms and parallelization strategies, ultimately enabling the study of increasingly complex chemical systems.