Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry ||1
Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry
By Nohil Kodiyatar
ORCID iD: https://orcid.org/0000-0001-8430-1641
Contact: nohil3689@gmail.com
DOI: 10.5281/zenodo.15502939
Part of Book: Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry
ISBN: 979-8-285-13304-9
Abstract
This book explores the transformative integration of Artificial Intelligence (AI) into theoretical and computational chemistry. It examines how AI technologies, particularly machine learning and deep learning, are revolutionizing quantum simulations, wavefunction modeling, and AI-assisted spectroscopy. The work highlights the impact of AI on molecular design, materials science, and the development of autonomous laboratories, which automate experimental processes and accelerate scientific discovery.
Keywords:
Artificial Intelligence, Quantum Chemistry, Computational Chemistry, Machine Learning, Wavefunction Prediction, FermiNet, OrbNet, Quantum Machine Learning, Theoretical Chemistry, Molecular Modeling
Introduction
Theoretical and computational chemistry have traditionally struggled with the computational complexity of quantum mechanical problems. The introduction of AI, however, is changing this landscape by enabling more efficient and accurate scientific discovery. This book provides a comprehensive examination of AI's role in enhancing the capabilities of theoretical chemistry.
AI in Theoretical Chemistry
Quantum Simulations and Wavefunction Modeling
AI-driven quantum simulations enhance the ability to model wavefunctions and predict molecular properties, providing unprecedented accuracy and efficiency.
Machine Learning and Quantum Chemistry
Machine learning models improve the accuracy and efficiency of quantum chemistry simulations by predicting molecular behaviors based on large datasets.
Applications and Advancements
Molecular and Materials Science
AI optimizes wavefunctions and predicts unknown species' properties, revolutionizing molecular design and materials development.
Challenges and Future Directions
Despite its potential, AI in theoretical chemistry faces challenges like data scarcity and interpretability of AI-generated results. Future developments aim to integrate quantum computing with AI to enhance computational capabilities.
Conclusion
AI's integration into theoretical and computational chemistry marks a transformative era, revolutionizing how scientists tackle complex chemical problems. This synergy between AI technologies and traditional methodologies accelerates discoveries in drug design, materials science, and beyond.
Citation
Kodiyatar, N. (2025). Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry. Zenodo. https://doi.org/10.5281/zenodo.15502939
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Notes
This article is part of a larger book: Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry (ISBN: 979-8-285-13304-9). All chapters are individually assigned DOIs and can be cited separately.
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