The structure of liquid methanol revisited: a neutron diffraction experiment at -80 °C and +25 °C

Yamaguchi, Toshio; Hidaka, Kazuko; Soper, A. K.

doi:10.1080/00268979909482859

Cited by 89 publications

(41 citation statements)

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Section: Figuresupporting

confidence: 55%

“…The radial distribution function of methanol determined from neutron scattering 69 compares favorably with the present force field model. 73 The O-H hydrogen bonding peak is, however, higher than the experiment. This may reflect the neglect of quantum effects of the nuclei in the simulations, 25 as well as inaccuracies in the experimentally determined pair correlation functions.…”

Section: Figuresupporting

confidence: 55%

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Machine Learning Force Field Parameters from Ab Initio Data

Liu

Pickard

et al. 2017

J. Chem. Theory Comput.

129

102

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Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to explore a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4,943 dimers electrostatic potentials and 1,250 clusters interaction energies for methanol. Excellent agreement between the training dataset from QM calculations and the optimized force field model can be achieved. Better results are achieved by introducing an offset factor during the machine learning process to compensate for the discrepancy of the QM calculated energy and the energy reproduced by optimized force field, where the offset factor maintain the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force field parameters described here could easily be extended to other molecular systems.

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Section: Figuresupporting

confidence: 55%

Section: Figuresupporting

confidence: 55%

Machine Learning Force Field Parameters from Ab Initio Data

Liu

Pickard

et al. 2017

J. Chem. Theory Comput.

129

102

View full text Add to dashboard Cite

show abstract

“…55,56 The topology adjustments in the final DC model make the liquid more structured, but they do little to the radial location of the existing methanol structure of the starting model.…”

Section: Resultsmentioning

confidence: 99%

A Fixed-Charge Model for Alcohol Polarization in the Condensed Phase, and Its Role in Small Molecule Hydration

2014

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We present a simple optimization strategy for incorporating experimental dielectric response information on neat liquids in classical molecular models of alcohol. Using this strategy, we determine simple and transferable hydroxyl modulation rules that, when applied to an existing molecular parameter set, result in a newly dielectric corrected (DC) parameter set. We applied these rules to the general Amber force field (GAFF) to form an initial set of GAFF-DC parameters, and we found this to lead to significant improvement in the calculated dielectric constant and hydration free energy values for a wide variety of small molecule alcohol models. Tests of the GAFF-DC parameters in the SAMPL4 blind prediction event for hydration show these changes improve agreement with experiment. Surprisingly, these simple modifications also outperform detailed quantum mechanical electric field calculations using a self-consistent reaction field environment coupling term. This work provides a potential benchmark for future developments in methods for representing condensed-phase environments in electronic structure calculations.

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“…Partial charges of atoms in the ligands and complexes were obtained from separate DFT/B3LYP calculations using the optimized structures and the ChelpG method,43 and were distributed over the FD grids using cubic B-spline discretization. Atomic radii required for defining solvent exclusion regions were taken from Stefanovich & Truong;44 and the diameters of the solvent molecules required to create the molecular surfaces on the FD grids were taken as the distances corresponding to the principal maxima of their oxygen-oxygen pair distribution functions in liquid state, that is, 1.4 Å for water, 1.4 Å for methanol45 and 2.15 Å for formamide 46. The radii needed to partition the inner- and outer-shell domains were taken as 2.6 Å for Na + ions and 3.1 Å for K + ions.…”

Section: Methodsmentioning

confidence: 99%

Structural Transitions in Ion Coordination Driven by Changes in Competition for Ligand Binding

2008

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Transferring Na + and K + ions from their preferred coordination states in water to states having different coordination numbers incurs a free energy cost. In several examples in nature, however, these ions readily partition from aqueous-phase coordination states into spatial regions having much higher coordination numbers. Here we utilize statistical theory of solutions, quantum chemical simulations, classical mechanics simulations and structural informatics to understand this aspect of ion partitioning. Our studies lead to the identification of a specific role of the solvation environment in driving transitions in ion coordination structures. Although ion solvation in liquid media is an exergonic reaction overall, we find it is also associated with considerable free energy penalties for extracting ligands from their solvation environments to form coordinated ion complexes. Reducing these penalties increases the stabilities of higher-order coordinations and brings down the energetic cost to partition ions from water into over-coordinated binding sites in biomolecules. These penalties can be lowered via a reduction in direct favorable interactions of the coordinating ligands with all atoms other than the ions themselves. A significant reduction in these penalties can, in fact, also drive up ion coordination preferences. Similarly, an increase in these penalties can lower ion coordination preferences, akin to a Hofmeister effect. Since such structural transitions are effected by the properties of the solvation phase, we anticipate that they will also occur for other ions. The influence of other factors, including ligand density, ligand chemistry and temperature, on the stabilities of ion coordination structures are also explored.

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The structure of liquid methanol revisited: a neutron diffraction experiment at -80 °C and +25 °C

Cited by 89 publications

References 0 publications

Machine Learning Force Field Parameters from Ab Initio Data

Machine Learning Force Field Parameters from Ab Initio Data

A Fixed-Charge Model for Alcohol Polarization in the Condensed Phase, and Its Role in Small Molecule Hydration

Structural Transitions in Ion Coordination Driven by Changes in Competition for Ligand Binding

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