Advanced Computational Chemistry with Python: A Guide to Quantum Simulations and Machine Learning Potentials

Reactive Publishing Advanced Computational Chemistry with Python bridges the gap between theoretical physical chemistry and scalable, production-grade code. Designed for modern computational chemists, materials scientists, and graduate-level researchers, this comprehensive guide demonstrates how to implement state-of-the-art algorithms using the Python scientific ecosystem.Inside, you will find hands-on implementations and theoretical foundations covering: - Ab Initio Molecular Dynamics (AIMD): Simulating real-time chemical dynamics directly from electronic structure calculations.- Machine Learning Interatomic Potentials: Building and training neural network potentials (NNPs) to achieve quantum-level accuracy at a fraction of the computational cost.- Large-Scale Quantum Simulations: Parallelizing electronic structure routines and leveraging acceleration frameworks for multi-thousand atom systems.- Python Engineering Best Practices: Structuring performance-critical scientific pipelines with NumPy, SciPy, PyTorch, and specialized quantum chemistry libraries. Whether you are scaling up quantum mechanical simulations or applying modern AI to chemical modeling, this book provides the practical framework needed to build, optimize, and deploy advanced computational chemistry pipelines.