Tag: python

Combining mechanistic and statistical models for predicting reaction outcomes in organic synthesis.

Gallegos, L. C. Colorado State University 2023

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries.

Sowndarya, S. S. V.; Law, J.; Tripp, C.; Duplyakin, D.; Skordilis, E.; Biagioni, D.; Paton, R. S.; St. John, P. C. Nat. Mach. Intell. 2022, 7, 720–730

Homologation of Electron-Rich Benzyl Bromide Derivatives via Diazo C–C Bond Insertion.
Modak, A.; Alegre-Requena, J. V.; Lescure, L.; Rynders, K. J.; Paton, R. S.; Race, N. J. Am. Chem. Soc. 2022, 144, 86–92

A Python program to compute quasi-harmonic thermochemical data and potential energy surface diagrams from frequency calculations at a given temperature/concentration, corrected for the effects of vibrational scaling-factors. All (electronic, translational, rotational and vibrational) partition functions are recomputed and can be correct to any temperature or concentration. The first public version of GoodVibes was released in 2016 and it has undergone several revisions since, during which time it has been used by many groups around the world. The program is described in the publication: GoodVibes: automated thermochemistry for heterogeneous computational chemistry data

[Zenodo] [GitHub]

A program to generate Boltzmann-weighted Sterimol Steric Parameters for conformationally-flexible substituents that integrates with PyMol. The program contains an automated computational workflow which computes multidimensional Sterimol parameters. For flexible molecules or substituents, the program will generate & optimize a conformational ensemble, and produce Boltzmann-weighted Sterimol parameters. It has been developed as a PyMol plugin and can be run from within the graphical user interface. The wSterimol code is described in more detail in Conformational Effects on Physical-Organic Descriptors – the Case of Sterimol Steric Parameters

[Zenodo] [GitHub]
GoodVibes: automated thermochemistry for heterogeneous computational chemistry data.

Luchini, G.; Alegre-Requena, J. V.; Funes-Ardoiz, I.; Paton, R. S. F1000Research 2020, 9, 291


A Python program to compute kinetic isotope effects from two Gaussian output files, one of which is a ground state and the other a transition state. The level of theory and basis set are detected from in the output files and the program will attempt to assign the appropriate scaling factor based on data from the Truhlar group. The program diagonalizes the mass-weighted Hessian matrices to obtain harmonic vibrational frequencies and Bigeleisen-Mayer Reduced Isotopic Partition Function Ratios. Kinisot is freely available (CC-BY license) from GitHub; alternatively, it can be installed from the command line with pipy: pip install kinisot.

[Zenodo] [GitHub]

A standalone Python version of Grimme’s DFT-D3 dispersion correction with zero and Becke-Johnson damping. This python version will give D3 energies identical to other implementations in electronic structure codes,  but these can also be decomposed into interatomic or intermolecular terms. If separate molecules are recognized, based on interatomic connectivity, then the intramolecular terms can also be ignored.  The pyDFTD3 code was largely written for Gaussian formatted input and output files, and should be able to parse the functional from these and apply the appropriate damping parameters automatically. Otherwise they can be specified manually from the command line.

[Zenodo] [GitHub]
Development of a True Transition State Force Field (TTSFF) from Quantum Mechanical Calculations.

Madarász, A.; Berta, D.; Paton, R. S. J. Chem. Theor. Comput. 2016, 12, 1833–1844