Comparing the Performances of Force Fields in Conformational Searching of Hydrogen-Bond-Donating Catalysts

Here, we compare the relative performances of different force fields for conformational searching of hydrogen-bond-donating catalyst-like molecules. We assess the force fields by their predictions of conformer energies, geometries, low-energy, nonredundant conformers, and the maximum numbers of possible conformers. Overall, MM3, MMFFs, and OPLS3e had consistently strong performances and are recommended for conformationally searching molecules structurally similar to those in this study

Machine learning activation energies of chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar

Dataset for "Comparing the Performances of Force Fields in Conformational Searching of Hydrogen Bond-Donating Catalysts"

This dataset contains all of the fully optimised structures (from force field conformational searches and DFT) used in the work titled "Comparing the Performances of Force Fields in Conformational Searching of Hydrogen Bond-Donating Catalysts". Twenty organic molecules were conformationally searched with eight force fields (OPLS3e, OPLS-2005, MMFF, MMFFs, AMBER*, OPLS, MM2* and MM3*) and all of the conformer structures were geometry optimised at the M06-2X/6-31G(d) level of theory and single-point energies of the resulting minima were calculated with the M06-2X/def2-TZVPP level of theory. This dataset contains the .mol2 files that correspond to the conformer structures found by the force fields, and the .out files for the DFT-optimised minima and the single-point energies. Also provided are .csv files containing all of the conformer energies according to the force fields and DFT.Conformational searches were performed with the force fields available in Schrödinger’s MacroModel v12.6 (OPLS3e, OPLS-2005, MMFF, MMFFs, AMBER*, OPLS, MM2* and MM3*) on each of 20 molecules. Aside from the force field potentials, the other settings used in the conformational searches were as follows: solvent was set to “None” (gas-phase); the energy minimization method was set to “Polak-Ribiere Conjugate Gradient” (PRCG), converging on the gradient with convergence threshold set to 0.001; the search method was set to “Mixed torsional/Low-mode sampling” with the energy window for saving structures set to 50 kJ mol-1 and the maximum atom deviation for structures to be considered different conformers was set to 0.25 Å. All DFT calculations were performed using the Gaussian 16, Revision A.03 software. Geometry optimizations were performed on all conformers of all molecules at the M06-2X/6-31G(d) level of theory in the IEFPCM(benzene) solvent model. Once all of the conformer structures had reached minima and were stationary points, single-point energies were calculated at the M06-2X/def2-TZVPP IEFPCM(benzene) level of theory. This level of theory was chosen since it has previously been used with success in previous reaction modelling studies.Schrödinger MacroModel v12.6; Gaussian 16, Revision A.03Each of the 20 molecules in the dataset was given a nickname that is used for the calculation files. The molecule numbers (used in the publication) and 2D molecular structures that these nicknames correspond to can be found from the images "molecule_names_and_structures_1.jpg" and "molecule_names_and_structures_2.jpg". Within each molecule directory, there is one directory for each force field that was able to conformationally search that molecule (format: /). Within the directories are the .mol2 files that correspond to the force field structures from the conformational searches. As well as the force field structures, the directories each contain a "mo62x_6-31Gd" directory that contain the M06-2X/6-31G(d) optimised minima as Gaussian .out files and the M06-2X/def2-TZVPP//M06-2X/6-31G(d) single-point energy calculations as "_SPEopt.out" files. The "csvs" directory contains one .csv file for each molecule, each containing all of the force field and DFT energetic data for all conformers from each force field. The columns of each .csv data file are: conf: The conformer numbers from the searches FFE: The energies of the conformers accourding to the force fields Structure: The filenames of the DFT minima E_SPC: The single-point corrected DFT energy E: The uncorrected M06-2X/6-31G(d) energy ZPE: The zero-point energy H_SPC: The single-point corrected enthalpy T.S: The product of the temperature (298.15 K) and the entropy T.qh-S: The product of the temperature and the quasi-harmonic entropy G(T)_SPC: The single-point corrected Gibbs free energy qh-G(T)_SPC: The single-point corrected quasi-harmonic Gibbs free energ

Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"

This dataset contains the Gaussian 16 output files for the dataset of aza-Michael addition reactions used in the publication "Fast Identification of Reactions with Desired Barriers by Reformulating Machine Learning Activation Energies". The structures of the methylamine nucleophile, the 1000 Michael acceptor electrophiles and their 1000 transition states were all optimised at the wB97X-D/def2-TZVP level of theory with the IEFPCM(water) implicit solvent model. Before optimisation all Michael acceptors and transition states were conformationally searched using the MMFF force field in Schrödinger's MacroModel software and the lowest energy conformer was selected for DFT calculation. This dataset also contains the Gaussian 16 output files for the SVWN/def2-SVP single-point energy calculations on the dihydrogen activation catalyst and transition state structures.1000 Michael acceptor structures and their transition states for their reactions with methylamine were generated according the the scheme shown in the image "michael_structures.png" using the “R-Group Creator” and “Custom R-Group Enumeration” tools from Schrödinger's Maestro. The resulting Michael acceptors and transition states were conformationally searched using Schrödinger's MacroModel with the MMFF force field and the lowest energy electrophile and transition state conformers were selected for DFT optimisation. Gaussian 16 was used to perform geometry optimisation of the selected conformers as well as the methylamine nucleophile at the wB97X-D/def-TZVP level of theory with the IEFPCM(water) solvent model. Gaussian 16 was also used to perform single-point energy calculations on the Michael acceptor and transition state structures using the PM6 semi-empirical method with the IEFPCM(water) solvent model. Gaussian 16 was used to perform single-point energy calculations at the SVWN/def2-SVP level of theory on all of the transition state and catalyst structures available from the "Vaska's space" dataset (https://doi.org/10.5683/SP2/CJS7QA).“R-Group Creator” and “Custom R-Group Enumeration” tools from Schrödinger Maestro v12.5. “Conformational Search” tool from Schrödinger MacroModel v12.9. Gaussian 16, Revision A.03 and Revision C.01.The "electrophiles.zip" file contains the Gaussian output files for the optimised Michael acceptor structures. The "transitionstates.zip" file contains the Gaussian output files for the optimised aza-Michael addition transition state structures. The "methylamine.out" file is the Gaussian output file for the optimised methylamine nucleophile structure. The "electrophiles_pm6.zip" file contains the Gaussian output files for the PM6 single-point energies for the Michael acceptors. The "transitionstates_pm6.zip" file contains the Gaussian output files for the PM6 single-point energies for aza-Michael addtion transition states. The "methylamine_pm6.out" file is the Gaussian output file for the PM6-optimised methylamine nucleophile structure. The "catalysts_lda.zip" file contains the Gaussian output files for the single-point LDA iridium catalyst energies. The "dihydrogen_lda.zip" file contains the Gaussian output files for the single-point LDA dihydrogen activation transition state energies. The "h2.out" file is the Gaussian output file for the LDA-optimised dihydrogen molecule

Comparisons of Different Force Fields in Conformational Analysis and Searching of Organic Molecules: A Review

This review aims to examine literature where different force fields are compared by their performances in conformational analysis and searching of organic molecules. Conformational analysis studies are those where energies and/or geometries of conformers are evaluated with force fields; the closer the values are to experiment or ab initio calculations, the better the force field performance. In conformational searching, an algorithm alters the geometry of a chemical system, followed by force field energy minimisation, then the process repeats, ideally until all conformations of the system are found. For conformational analysis, MM2, MM3 and MMFF94 often showed strong performances and their use is recommended. The polarisable AMOEBA force field consistently had strong performance and further comparisons including AMOEBA are advised. UFF showed very weak performance and is not recommended. For conformational searching, a distinct lack of comparisons were found, and the need for more work is emphasised

Dataset for "Comparing the Performances of Force Fields in Conformational Searching of Hydrogen Bond-Donating Catalysts"

This dataset contains all of the fully optimised structures (from force field conformational searches and DFT) used in the work titled "Comparing the Performances of Force Fields in Conformational Searching of Hydrogen Bond-Donating Catalysts". Twenty organic molecules were conformationally searched with eight force fields (OPLS3e, OPLS-2005, MMFF, MMFFs, AMBER*, OPLS, MM2* and MM3*) and all of the conformer structures were geometry optimised at the M06-2X/6-31G(d) level of theory and single-point energies of the resulting minima were calculated with the M06-2X/def2-TZVPP level of theory. This dataset contains the .mol2 files that correspond to the conformer structures found by the force fields, and the .out files for the DFT-optimised minima and the single-point energies. Also provided are .csv files containing all of the conformer energies according to the force fields and DFT

Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"

This dataset contains the Gaussian 16 output files for the dataset of aza-Michael addition reactions used in the publication "Fast Identification of Reactions with Desired Barriers by Reformulating Machine Learning Activation Energies". The structures of the methylamine nucleophile, the 1000 Michael acceptor electrophiles and their 1000 transition states were all optimised at the wB97X-D/def2-TZVP level of theory with the IEFPCM(water) implicit solvent model. Before optimisation all Michael acceptors and transition states were conformationally searched using the MMFF force field in Schrödinger's MacroModel software and the lowest energy conformer was selected for DFT calculation. This dataset also contains the Gaussian 16 output files for the SVWN/def2-SVP single-point energy calculations on the dihydrogen activation catalyst and transition state structures