With dual goals of efficient and accurate modeling of solvation thermodynamics in molten salt liquids, we employ ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT) to calculate the excess chemical potentials for the solute ions Na + and Cl − in the molten NaCl liquid. NNIP-based molecular dynamics simulations accelerate the calculations by 3 orders of magnitude and reduce the uncertainty to 1 kcal mol −1 . Using the Density Functional Theory (DFT) level of theory, the predicted excess chemical potential for the solute ion pair is −178.5 ± 1.1 kcal mol −1 . A quantum correction of 13.7 ± 1.9 kcal mol −1 is estimated via higher-level quantum chemistry calculations, leading to a final predicted ion pair excess chemical potential of −164.8 ± 2.2 kcal mol −1 . The result is in good agreement with a value of −163.5 kcal mol −1 obtained from thermo-chemical tables. This study validates the application of QCT and NNIP simulations to the molten salt liquids, allowing for significant insights into the solvation thermodynamics crucial for numerous molten salt applications.
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Accelerating the 3D reference interaction site model theory of molecular solvation with treecode summation and cut‐offs
The 3D reference interaction site model (3D‐RISM) of molecular solvation is a powerful tool for computing the equilibrium thermodynamics and density distributions of solvents, such as water and co‐ions, around solute molecules. However, 3D‐RISM solutions can be expensive to calculate, especially for proteins and other large molecules where calculating the potential energy between solute and solvent requires more than half the computation time. To address this problem, we have developed and implemented treecode summation for long‐range interactions and analytically corrected cut‐offs for short‐range interactions to accelerate the potential energy and long‐range asymptotics calculations in non‐periodic 3D‐RISM in the AmberTools molecular modeling suite. For the largest single protein considered in this work, tubulin, the total computation time was reduced by a factor of 4. In addition, parallel calculations with these new methods scale almost linearly and the iterative solver remains the largest impediment to parallel scaling. To demonstrate the utility of our approach for large systems, we used 3D‐RISM to calculate the solvation thermodynamics and density distribution of 7‐ring microtubule, consisting of 910 tubulin dimers, over 1.2 million atoms.
more » « less- NSF-PAR ID:
- 10367685
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 43
- Issue:
- 18
- ISSN:
- 0192-8651
- Page Range / eLocation ID:
- p. 1251-1270
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Although cyclic voltammetry (CV) measurements in solution have been widely used to determine the highest occupied molecular orbital energy (
E HOMO) of semiconducting organic molecules, an understanding of the experimentally observed discrepancies due to the solvent used is lacking. To explain these differences, we investigate the solvent effects onE HOMOby combining density functional theory and molecular dynamics calculations for four donor molecules with a common backbone moiety. We compare the experimentalE HOMOvalues to the calculated values obtained from either implicit or first solvation shell theories. We find that the first solvation shell method can capture theE HOMOvariation arising from the functional groups in solution, unlike the implicit method. We further applied the first solvation shell method to other semiconducting organic molecules measured in solutions for different solvents. We find that theE HOMOobtained using an implicit method is insensitive to solvent choice. The first solvation shell, however, producesE HOMOvalues that are sensitive to solvent choices and agrees with published experimental results. The solvent sensitivity arises from a hierarchy of three effects: (1) the solute electronic state within a surrounding dielectric continuum, (2) ambient temperature or solvent atoms changing the solute geometry, and (3) electronic interactions between the solute and solvents. The implicit method, on the other hand, only captures the effect of a dielectric environment. Our findings suggest thatE HOMOobtained by CV measurements should account for the influence of solvent when the results are reported, interpreted, or compared to other molecules. -
Hydration free energies of small molecules are commonly used as benchmarks for solvation models. However, errors in predicting hydration free energies are partially due to the force fields used and not just the solvation model. To address this, we have used the 3D reference interaction site model (3D-RISM) of molecular solvation and existing benchmark explicit solvent calculations with a simple element count correction (ECC) to identify problems with the non-bond parameters in the general AMBER force field (GAFF). 3D-RISM was used to calculate hydration free energies of all 642 molecules in the FreeSolv database, and a partial molar volume correction (PMVC), ECC, and their combination (PMVECC) were applied to the results. The PMVECC produced a mean unsigned error of 1.01±0.04kcal/mol and root mean squared error of 1.44±0.07kcal/mol, better than the benchmark explicit solvent calculations from FreeSolv, and required less than 15 s of computing time per molecule on a single CPU core. Importantly, parameters for PMVECC showed systematic errors for molecules containing Cl, Br, I, and P. Applying ECC to the explicit solvent hydration free energies found the same systematic errors. The results strongly suggest that some small adjustments to the Lennard–Jones parameters for GAFF will lead to improved hydration free energy calculations for all solvent models.more » « less
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Many biomolecules undergo conformational changes associated with allostery or ligand binding. Observing these changes in computer simulations is difficult if their timescales are long. These calculations can be accelerated by observing the transition on an auxiliary free energy surface with a simpler Hamiltonian and connecting this free energy surface to the target free energy surface with free energy calculations. Here, we show that the free energy legs of the cycle can be replaced with energy representation (ER) density functional approximations. We compute: (1) The conformational free energy changes for alanine dipeptide transitioning from the right‐handed free energy basin to the left‐handed basin and (2) the free energy difference between the open and closed conformations of
β ‐cyclodextrin, a “host” molecule that serves as a model for molecular recognition in host‐guest binding.β ‐cyclodextrin contains 147 atoms compared to 22 atoms for alanine dipeptide, makingβ ‐cyclodextrin a large molecule for which to compute solvation free energies by free energy perturbation or integration methods and the largest system for which the ER method has been compared to exact free energy methods. The ER method replaced the 28 simulations to compute each coupling free energy with two endpoint simulations, reducing the computational time for the alanine dipeptide calculation by about 70% and for theβ ‐cyclodextrin by > 95%. The method works even when the distribution of conformations on the auxiliary free energy surface differs substantially from that on the target free energy surface, although some degree of overlap between the two surfaces is required. © 2016 Wiley Periodicals, Inc. -
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Abstract The Poisson‐Boltzmann equation is a widely used model to study electrostatics in molecular solvation. Its numerical solution using a boundary integral formulation requires a mesh on the molecular surface only, yielding accurate representations of the solute, which is usually a complicated geometry. Here, we utilize adjoint‐based analyses to form two goal‐oriented error estimates that allow us to determine the contribution of each discretization element (panel) to the numerical error in the solvation free energy. This information is useful to identify high‐error panels to then refine them adaptively to find optimal surface meshes. We present results for spheres and real molecular geometries, and see that elements with large error tend to be in regions where there is a high electrostatic potential. We also find that even though both estimates predict different total errors, they have similar performance as part of an adaptive mesh refinement scheme. Our test cases suggest that the adaptive mesh refinement scheme is very effective, as we are able to reduce the error one order of magnitude by increasing the mesh size less than 20% and come out to be more efficient than uniform refinement when computing error estimations. This result sets the basis toward efficient automatic mesh refinement schemes that produce optimal meshes for solvation energy calculations.
