Transferable force fields and transport properties

PN 3-8

Project description

Classical transferable force fields allow predicting physical properties and microscopic processes of pure substances and mixtures. The development of force fields, especially the parametrization of the ‘non-bonded’ interactions, requires multidimensional optimization with computationally expensive (and noisy) objective function evaluations through Monte Carlo (MC) and molecular dynamics (MD) simulations. This is demanding because force-field parameters are highly correlated. We use physically-based surrogate models to drastically accelerate the optimization procedure and specifically propose new reduced order models for transport properties based on entropy scaling. Transport properties, such as viscosity, thermal conductivity, and self-diffusion coefficients, can then be considered in the optimization of force fields simultaneously with static properties, which was not possible before. Machine-learned models for transport properties will be developed with an increasing data-base of simulated force field parameters and substances.

Project information

Project title Transferable force fields and transport properties
Project leaders Niels Hansen, Joachim Groß
Project partners Jürgen Pleiss
Christian Holm
Project duration February 2020 - June 2023
Project number PN 3-8

Publications PN 3-8 and PN 3-8 (II)

  1. 2023

    1. L. Neumaier, D. Roskosch, J. Schilling, G. Bauer, J. Gross, and A. Bardow, “Refrigerant Selection for Heat Pumps: The Compressor Makes the Difference,” Energy Technology, Feb. 2023, doi: 10.1002/ente.202201403.
    2. M. Hammer, G. Bauer, R. Stierle, J. Groß, and Ø. Wilhelmsen, “Classical density functional theory for interfacial properties of hydrogen, helium, deuterium, neon, and their mixtures,” The Journal of Chemical Physics, vol. 158, no. 10, Art. no. 10, 2023, doi: 10.1063/5.0137226.

  2. 2022

    1. J. Eller, T. Sauerborn, B. Becker, I. Buntic, J. Gross, and R. Helmig, “Modelling Subsurface Hydrogen Storage with Transport Properties from Entropy Scaling using the PC-SAFT Equation of State,” Water Resources Research, Apr. 2022, doi: 10.1029/2021wr030885.
    2. M. Pechlaner, W. F. van Gunsteren, N. Hansen, and L. J. Smith, “Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme,” European Biophysics Journal, vol. 51, no. 3, Art. no. 3, Apr. 2022, doi: 10.1007/s00249-022-01593-1.
    3. W. F. van Gunsteren, M. Pechlaner, L. J. Smith, B. Stankiewicz, and N. Hansen, “A Method to Derive Structural Information on Molecules from Residual Dipolar Coupling NMR Data,” The Journal of Physical Chemistry B, vol. 126, no. 21, Art. no. 21, May 2022, doi: 10.1021/acs.jpcb.2c02410.
    4. N. E. R. Zimmermann, G. Guevara-Carrion, J. Vrabec, and N. Hansen, “Predicting and Rationalizing the Soret Coefficient of Binary Lennard-Jones Mixtures in the Liquid State,” Advanced Theory and Simulations, vol. 5, no. 11, Art. no. 11, Jul. 2022, doi: 10.1002/adts.202200311.
    5. D. Markthaler, H. Kraus, and N. Hansen, “Binding free energies for the SAMPL8 CB8 ‘Drugs of Abuse’ challenge from umbrella sampling combined with Hamiltonian replica exchange,” Journal of Computer-Aided Molecular Design, vol. 36, pp. 1–9, 2022, doi: 10.1007/s10822-021-00439-w.
    6. D. Markthaler, M. Fleck, B. Stankiewicz, and N. Hansen, “Exploring the Effect of Enhanced Sampling on Protein Stability Prediction,” Journal of Chemical Theory and Computation, vol. 18, no. 4, Art. no. 4, Mar. 2022, doi: 10.1021/acs.jctc.1c01012.
    7. C. Kessler et al., “Influence of layer slipping on adsorption of light gases in covalent organic frameworks: A combined experimental and computational study,” Microporous and Mesoporous Materials, vol. 336, p. 111796, May 2022, doi: 10.1016/j.micromeso.2022.111796.
    8. L. Neumaier, J. Schilling, A. Bardow, and J. Gross, “Dielectric constant of mixed solvents based on perturbation theory,” Fluid Phase Equilibria, vol. 555, p. 113346, Apr. 2022, doi: 10.1016/j.fluid.2021.113346.
    9. T. van Westen, M. Hammer, B. Hafskjold, A. Aasen, J. Gross, and Ø. Wilhelmsen, “Perturbation theories for fluids with short-ranged attractive forces: A case study of the Lennard-Jones spline fluid,” The Journal of Chemical Physics, vol. 156, no. 10, Art. no. 10, Mar. 2022, doi: 10.1063/5.0082690.
    10. P. Rehner, T. van Westen, and J. Gross, “Equation of state and Helmholtz energy functional for fused heterosegmented hard chains,” Physical Review E, Mar. 2022, doi: 10.1103/PhysRevE.105.034110.

  3. 2021

    1. L. J. Smith, W. F. van Gunsteren, B. Stankiewicz, and N. Hansen, “On the use of 3J-coupling NMR data to derive structural information on proteins,” Journal of Biomolecular NMR, vol. 75, no. 1, Art. no. 1, Jan. 2021, doi: 10.1007/s10858-020-00355-5.
    2. J. Eller and J. Gross, “Free-Energy-Averaged Potentials for Adsorption in Heterogeneous Slit Pores Using PC-SAFT Classical Density Functional Theory,” Langmuir, vol. 37, no. 12, Art. no. 12, Mar. 2021, doi: 10.1021/acs.langmuir.0c03287.
    3. C. Keßler, J. Eller, J. Groß, and N. Hansen, “Adsorption of light gases in covalent organic frameworks : comparison of classical density functional theory and grand canonical Monte Carlo simulations,” Microporous and mesoporous materials, vol. 324, no. September, Art. no. September, 2021, doi: 10.1016/j.micromeso.2021.111263.
    4. J. Eller, T. Matzerath, T. van Westen, and J. Gross, “Predicting solvation free energies in non-polar solvents using classical density functional theory based on the PC-SAFT equation of state,” The Journal of Chemical Physics, Jun. 2021, doi: 10.1063/5.0051201.
    5. T. van Westen and J. Gross, “Accurate first-order perturbation theory for fluids: uf-theory,” The Journal of Chemical Physics, Jan. 2021, doi: 10.1063/5.0031545.
    6. D. Markthaler and N. Hansen, “Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B,” Data in Brief, vol. 39, p. 107618, Dec. 2021, doi: 10.1016/j.dib.2021.107618.
    7. T. van Westen and J. Gross, “Accurate thermodynamics of simple fluids and chain fluids based on first-order perturbation theory and second virial coefficients: uv-theory,” The Journal of Chemical Physics, Dec. 2021, doi: 10.1063/5.0073572.
    8. R. Stierle and J. Gross, “Hydrodynamic density functional theory for mixtures from a variational principle and its application to droplet coalescence,” The Journal of Chemical Physics, Oct. 2021, doi: 10.1063/5.0060088.
    9. P. Rehner, B. Bursik, and J. Gross, “Surfactant Modeling Using Classical Density Functional Theory and a Group Contribution PC-SAFT Approach,” Industrial & Engineering Chemistry Research, vol. 60, no. 19, Art. no. 19, Apr. 2021, doi: 10.1021/acs.iecr.1c00169.
    10. L. J. Smith, W. F. Gunsteren, and N. Hansen, “On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme,” ChemBioChem, vol. 22, no. 6, Art. no. 6, Dec. 2021, doi: 10.1002/cbic.202000674.

  4. 2020

    1. N. Hansen et al., “A Suite of Advanced Tutorials for the GROMOS Biomolecular Simulation Software Article v1.0,” Living Journal of Computational Molecular Science, vol. 2, no. 1, Art. no. 1, 2020, doi: 10.33011/livecoms.2.1.18552.
    2. J. Gebhardt, M. Kiesel, S. Riniker, and N. Hansen, “Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients,” Journal of Chemical Information and Modeling, vol. 60, no. 11, Art. no. 11, Aug. 2020, doi: 10.1021/acs.jcim.0c00479.
    3. P. Rehner and J. Gross, “Multiobjective Optimization of PCP-SAFT Parameters for Water and Alcohols Using Surface Tension Data,” Journal of Chemical & Engineering Data, vol. 65, no. 12, Art. no. 12, Sep. 2020, doi: 10.1021/acs.jced.0c00684.

  5. 2019

    1. M. Hopp and J. Gross, “Thermal Conductivity from Entropy Scaling: A Group-Contribution Method,” Industrial & Engineering Chemistry Research, vol. 58, no. 44, Art. no. 44, Oct. 2019, doi: 10.1021/acs.iecr.9b04289.
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