Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry

Title: Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry

ISBN: 979-8-285-13304-9

Author: Nohil Kodiyatar
ORCID iD: https://orcid.org/0000-0001-8430-1641

Description:
Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry is a groundbreaking academic book that explores the powerful intersection between artificial intelligence (AI) and quantum chemistry. It offers a detailed and forward-thinking synthesis of theoretical frameworks, algorithmic methodologies, and computational applications, designed for the evolving landscape of 21st-century molecular science.

This work begins by establishing a strong foundation in quantum mechanics, covering core topics such as the Schrödinger equation, wavefunction theory, electronic structure methods (Hartree-Fock, DFT, CI), and potential energy surface (PES) modeling. From there, it transitions into a thorough analysis of how AI—particularly machine learning (ML), deep learning, graph neural networks (GNNs), and reinforcement learning—is being applied to complex chemical problems that were previously intractable.

Key highlights include:

• Detailed coverage of advanced AI models such as FermiNet, PauliNet, OrbNet, and SchNet, with explanations of their architectures, benchmarks, and performance in wavefunction learning and energy prediction.
• Practical insights into hybrid methods, including ML-augmented DFT, delta-learning, and AI-based correction models for quantum simulations.
• Applications in frontier domains such as drug discovery, materials science, computational spectroscopy, retrosynthesis planning, and autonomous laboratories.
• Exploration of future technologies including quantum machine learning, AI-complete automation, and the emergence of cognitive chemical systems capable of independent scientific reasoning and hypothesis generation.
• A rich collection of real-world datasets, benchmarking strategies, and interpretability frameworks to guide reproducibility and implementation in research and industry.

This book also critically discusses current challenges in AI-chemistry integration, such as data scarcity, model generalization, computational cost, and the trustworthiness of AI-generated wavefunctions. It proposes feasible solutions and sets the stage for future research and development.

Ideal for:
Graduate students, academic researchers, computational chemists, quantum physicists, AI practitioners in science, and interdisciplinary scholars who seek a rigorous, practical, and visionary resource in the evolving field of AI-enhanced theoretical chemistry.

Keywords:

Artificial Intelligence, Quantum Chemistry, Computational Chemistry, Machine Learning, Wavefunction Prediction, FermiNet, OrbNet, Quantum Machine Learning, Theoretical Chemistry, Molecular Modeling