credits.md

How to cite?

The Pople code is made openly available at https://github.com/moldis-group/pople_package with an MIT license. We welcome you to include it in your work. In return, we kindly ask you to cite the underlying entry in your work using Pople.

Sambit Kumar Das, Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan (2021) "Pople: A toolkit for ab initio thermochemistry" https://moldis-group.github.io/pople/

Bibtex entry

@article{das2021pople,
  title={Pople: A toolkit for ab initio thermochemistry},
  author={Das, SK and Senthil, S and Chakraborty, S and Ramakrishnan, R},
  journal={URL https://moldis-group.github.io/pople/},
  year={2021}
}

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Contributors

The program is developed by the following members of the Theory Lab at the Tata Institute of Fundamental Research Hyderabad, India

• Sambit Kumar Das
• Salini Senthil
• Sabyasachi Chakraborty
• Raghunathan Ramakrishnan

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Related publications

The Journal of Physical Chemistry A, 112(50), pp.12868-12886

PPE1694 dataset

Critical benchmarking of the G4(MP2) model, the correlation consistent composite approach and popular density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies
Sambit K. Das, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
J. Chem. Phys., 154 (2021) 044113.

G4(MP2)-XP method

All hands on deck: Accelerating ab initio thermochemistry via wavefunction approximations
Sambit K. Das, Salini Senthil, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
submitted (2021).

General references for ab initio thermochemistry and experimental results

This section will be constantly updated

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Composite Methods

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Gn Method

• Gaussian‐1 theory: A general procedure for prediction of molecular energies
  John A. Pople, Martin Head‐Gordon, Douglas J. Fox, Krishnan Raghavachari and Larry A. Curtiss
  J. Chem. Phys., 90 (1989) 5622

• Gaussian‐2 theory for molecular energies of first‐ and second‐row compounds
  Larry A. Curtiss, Krishnan Raghavachari, Gary W. Trucks and John A. Pople
  J. Chem. Phys., 94 (1991) 7221

• Gaussian-2 theory using reduced Mo/ller–Plesset orders
  Larry A. Curtiss, Krishnan Raghavachari and John A. Pople
  J. Chem. Phys., 98 (1993) 1293

• Assessment of Gaussian-2 and density functional theories for the computation of enthalpies of formation
  Larry A. Curtiss, Krishnan Raghavachari, Paul C. Redfern and John A. Pople
  J. Chem. Phys., 106 (1997) 1063

• Assessment of Gaussian-2 and density functional theories for the computation of ionization potentials and electron affinities
  Larry A. Curtiss, Paul C. Redfern, Krishnan Raghavachari and John A. Pople
  J. Chem. Phys., 109 (1998) 42 The Journal of Physical Chemistry A, 112(50), pp.12868-12886

• Gaussian-3 (G3) theory for molecules containing first and second-row atoms
  Larry A. Curtiss, Krishnan Raghavachari, Paul C. Redfern, Vitaly Rassolov and John A. Pople
  J. Chem. Phys., 109 (1998) 7764The Journal of Physical Chemistry A, 112(50), pp.12868-12886

• Gaussian-3 theory using density functional geometries and zero-point energies
  Anwar G. Baboul, Larry A. Curtiss, Paul C. Redfern and Krishnan Raghavachari
  J. Chem. Phys., 110 (1999) 7650

Wn Method

• Towards standard methods for benchmark quality ab initio thermochemistry—W1 and W2 theory
  Jan M. L. Martin and Glênisson de Oliveira
  J. Chem. Phys. 111, 1843 (1999)

• Unrestricted Coupled Cluster and Brueckner Doubles Variations of W1 Theory
  Ericka C. Barnes, George A. Petersson, John A. Montgomery, Jr.,Michael J. Frisch,and Jan M. L. Martin
  J. Chem. Theory Comput. 2009, 5, 10, 2687–2693

• Highly accurate first-principles benchmark data sets for the parametrization and validation of density functional and other approximate methods Derivation of a robust, generally applicable, double-hybrid functional for thermochemistry and thermochemical kinetics
  Karton, Amir, Alex Tarnopolsky, Jean-Francois Lamére, George C. Schatz, and Jan ML Martin
  J. Phys. Chem. A 2008, 112, 50, 12868–12886

• W4-11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data
  Karton, Amir, Shauli Daon, and Jan ML Martin
  Chemical Physics Letters 510, no. 4-6 (2011): 165-178

• W4-17: A Diverse and High-Confidence Dataset of Atomization Energies for Benchmarking High-Level Electronic Structure Methods
  Karton, Amir, Nitai Sylvetsky, and Jan ML Martin
  Journal of Computational Chemistry 38.24 (2017): 2063-2075

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Benchmarking

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• BH9, a New Comprehensive Benchmark Data Set for Barrier Heights and Reaction Energies: Assessment of Density Functional Approximations and Basis Set Incompleteness Potentials
  Viki Kumar Prasad, Zhipeng Pei, Simon Edelmann, Alberto Otero-de-la-Roza, and Gino A. DiLabio
  J. Chem. Theory Comput. 2022, 18, 151−166

• Tackling an accurate description of molecular reactivity with double-hybrid density functionals
  Éric Brémond, Hanwei Li, Ángel José Pérez-Jiménez, Juan Carlos Sancho-García, and Carlo Adamo
  J. Chem. Phys. 156, 161101 (2022)

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