Computational Chemistry for Experimentalists: A Nonspecialist's Guide to Practical and Predictive Simulations empowers chemists—especially those at emerging institutions or in small and medium enterprises—by transforming foundational chemical concepts into practical computational skills. A modular approach, paired with hands-on video tutorials, ensures that even nonspecialists can confidently apply simulations to their research, regardless of career stage or specialization. Beyond its accessible structure, the book features six modules covering core topics such as electronic structure theory and molecular dynamics. Ten experimental modules focus on simulating specific laboratory techniques—reaction mechanisms, NMR, UV/vis, band structures, XPS, and organometallic chemistry.
Regularly updated online tutorials complement the material, providing project-based, real-world training. By bridging theory and practice, this guide serves mid-career professionals, undergraduate and graduate students, and early-career researchers, making computational chemistry approachable and practical for all experimental chemists.
Ben's free online course complimenting this book is available on GitHub:
https://github.com/bjanesko/ComputationalChemistryForExperimentalists
1. 1. Introduction and motivation - p. 1 2. Section I. Core modules - p. S1 3. 2. Core chemical concepts - p. 9 4. 3. Basis sets and atomic orbitals - p. 23 5. 4. Self-consistent field molecular orbital calculations - p. 37 6. 5. Geometry optimization - p. 51 7. 6. The art of interpretation I: Charge densities, electrostatic potentials, and molecular orbitals - p. 63 8. 7. The art of interpretation II: Partial atomic charges, oxidation states, and bond orders - p. 75 9. 8. Dynamics and conformational sampling - p. 85 10. 9. Data processing - p. 97 11. Section II. Shared modules - p. S2 12. 10. Formation enthalpies and equilibrium constants - p. 113 13. 11. Transition states and reaction rates - p. 121 14. 12. Model solvent - p. 135 15. 13. Electronic structure approximations: Semiempirical, ab initio, and density functional theory - p. 145 16. 14. Chemical databases and machine learning - p. 161 17. Section III. Specific experiments - p. S3 18. 15. Ionization potentials, electron affinities, and redox potentials - p. 181 19. 16. Molecular vibrations, stationary points, and vibrational spectra - p. 193 20. 17. NMR spectra - p. 207 21. 18. Crystalline solids and band structures - p. 221 22. 19. pH-dependent processes - p. 239 23. 20. Electronic excitations and UV/vis spectroscopy - p. 251 24. Section IV. Summary examples - p. S4 25. 21. Modeling transition metal catalysis - p. 269 26. 22. Applications in drug design - p. 283 27. Index - p. 295
Computational Chemistry for Experimentalists: A Nonspecialist's Guide to Practical and Predictive Simulations empowers chemists—especially those at emerging institutions or in small and medium enterprises—by transforming foundational chemical concepts into practical computational skills. A modular approach, paired with hands-on video tutorials, ensures that even nonspecialists can confidently apply simulations to their research, regardless of career stage or specialization. Beyond its accessible structure, the book features six modules covering core topics such as electronic structure theory and molecular dynamics. Ten experimental modules focus on simulating specific laboratory techniques—reaction mechanisms, NMR, UV/vis, band structures, XPS, and organometallic chemistry.
Regularly updated online tutorials complement the material, providing project-based, real-world training. By bridging theory and practice, this guide serves mid-career professionals, undergraduate and graduate students, and early-career researchers, making computational chemistry approachable and practical for all experimental chemists.
Ben's free online course complimenting this book is available on GitHub:
https://github.com/bjanesko/ComputationalChemistryForExperimentalists
1. 1. Introduction and motivation - p. 1 2. Section I. Core modules - p. S1 3. 2. Core chemical concepts - p. 9 4. 3. Basis sets and atomic orbitals - p. 23 5. 4. Self-consistent field molecular orbital calculations - p. 37 6. 5. Geometry optimization - p. 51 7. 6. The art of interpretation I: Charge densities, electrostatic potentials, and molecular orbitals - p. 63 8. 7. The art of interpretation II: Partial atomic charges, oxidation states, and bond orders - p. 75 9. 8. Dynamics and conformational sampling - p. 85 10. 9. Data processing - p. 97 11. Section II. Shared modules - p. S2 12. 10. Formation enthalpies and equilibrium constants - p. 113 13. 11. Transition states and reaction rates - p. 121 14. 12. Model solvent - p. 135 15. 13. Electronic structure approximations: Semiempirical, ab initio, and density functional theory - p. 145 16. 14. Chemical databases and machine learning - p. 161 17. Section III. Specific experiments - p. S3 18. 15. Ionization potentials, electron affinities, and redox potentials - p. 181 19. 16. Molecular vibrations, stationary points, and vibrational spectra - p. 193 20. 17. NMR spectra - p. 207 21. 18. Crystalline solids and band structures - p. 221 22. 19. pH-dependent processes - p. 239 23. 20. Electronic excitations and UV/vis spectroscopy - p. 251 24. Section IV. Summary examples - p. S4 25. 21. Modeling transition metal catalysis - p. 269 26. 22. Applications in drug design - p. 283 27. Index - p. 295
