CoChat
Sign Up
Cited by 8Advanced Physical and Chemical Molecular InteractionsAdvanced Chemical Physics StudiesProtein Structure and Dynamics

OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking

Open access
Borgmans, Sander, Rogge, Sven M. J., Vanduyfhuys, Louis, Van Speybroeck, Veronique·2023·Journal of Chemical Theory and Computation
DOI 10.1021/acs.jctc.3c01028PMC PMC10753773

Published version of a preprint

Open this paper in CoChat — free account

Abstract

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acs.jctc.3c01028

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The minor deviations in the free energy profile lie well within the errors due to the sub-optimal set-up of the free energy method parameters.

    “Note, that such minor deviations are expected to lie well within the errors that are due to the sub-optimal set-up of the free energy method parameters (see Supplementary Fig. 12 or a related recent work76).”

    From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [76]

    The Benchmarking > 1D: Skewed Bimodal section (page unavailable) shows that free-energy-profile errors depend on the choice of method parameters, including the initial bias strength and grid spacing. It does not compare the citing paper’s minor deviations with those errors or establish that the deviations lie well within them. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 42

  1. Christ C. D.; Mark A. E.; van Gunsteren W. F. Basic ingredients of free energy calculations: A review. J. Comput. Chem. 2010, 31, 1569– 1582. 10.1002/jcc.21450. 20033914 · DOI
  2. Paul S.; Nair N. N.; Vashisth H. Phase space and collective variable based simulation methods for studies of rare events. Mol. Simul. 2019, 45, 1273– 1284. 10.1080/08927022.2019.1634268. · DOI
  3. Pal A.; Pal S.; Verma S.; Shiga M.; Nair N. N. Mean force based temperature accelerated sliced sampling: Efficient reconstruction of high dimensional free energy landscapes. J. Comput. Chem. 2021, 42, 1996– 2003. 10.1002/jcc.26727. 34398461 · DOI
  4. Tavernelli I.; Cotesta S.; Di Iorio E. E. Protein Dynamics, Thermal Stability, and Free-Energy Landscapes: A Molecular Dynamics Investigation. Biophys. J. 2003, 85, 2641– 2649. 10.1016/s0006-3495(03)74687-6. 14507727 PMC1303488 · DOI
  5. Shirts M. R. Computational Drug Discovery and Design; Baron R., Ed.; Springer New York: New York, NY, 2012; pp 425– 467.
  6. Borgmans S.; Rogge S. M. J.; De Vos J. S.; Van Der Voort P.; Van Speybroeck V. Exploring the phase stability in interpenetrated diamondoid covalent organic frameworks. Commun. Chem. 2023, 6, 5 10.1038/s42004-022-00808-y. 36698041 PMC9822923 · DOI
  7. Kirkwood J. G. Statistical Mechanics of Fluid Mixtures. J. Chem. Phys. 1935, 3, 300– 313. 10.1063/1.1749657. · DOI · Paper page
  8. Bennett C. H. Efficient estimation of free energy differences from Monte Carlo data. J. Comput. Phys. 1976, 22, 245– 268. 10.1016/0021-9991(76)90078-4. · DOI
  9. Torrie G.; Valleau J. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J. Comput. Phys. 1977, 23, 187– 199. 10.1016/0021-9991(77)90121-8. · DOI · Paper page
  10. Pohorille A.; Jarzynski C.; Chipot C. Good Practices in Free-Energy Calculations. J. Phys. Chem. B 2010, 114, 10235– 10253. 10.1021/jp102971x. 20701361 · DOI
  11. Klimovich P. V.; Shirts M. R.; Mobley D. L. Guidelines for the analysis of free energy calculations. J. Comput.-Aided Mol. Des. 2015, 29, 397– 411. 10.1007/s10822-015-9840-9. 25808134 PMC4420631 · DOI
  12. Morita A. Reference Module in Chemistry, Molecular Sciences and Chemical Engineering; Elsevier, 2017.
  13. Mezei M. Adaptive umbrella sampling: Self-consistent determination of the non-Boltzmann bias. J. Comput. Phys. 1987, 68, 237– 248. 10.1016/0021-9991(87)90054-4. · DOI
  14. Berne B. J.; Straub J. E. Novel methods of sampling phase space in the simulation of biological systems. Curr. Opin. Struct. Biol. 1997, 7, 181– 189. 10.1016/S0959-440X(97)80023-1. 9094324 · DOI
  15. Marsili S.; Barducci A.; Chelli R.; Procacci P.; Schettino V. Self-healing Umbrella Sampling: A Non-equilibrium Approach for Quantitative Free Energy Calculations. J. Phys. Chem. B 2006, 110, 14011– 14013. 10.1021/jp062755j. 16854090 · DOI
  16. Maragakis P.; van der Vaart A.; Karplus M. Gaussian-Mixture Umbrella Sampling. J. Phys. Chem. B 2009, 113, 4664– 4673. 10.1021/jp808381s. 19284746 PMC2806548 · DOI
  17. Rousset M.; Stoltz G.; Lelievre T. Free Energy Computations; Imperial College Press, 2010.
  18. Wojtas-Niziurski W.; Meng Y.; Roux B.; Bernèche S. Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions. J. Chem. Theory Comput. 2013, 9, 1885– 1895. 10.1021/ct300978b. 23814508 PMC3694627 · DOI
  19. Frenkel D.; Smit B. Understanding Molecular Simulation: From Algorithms to Applications; Elsevier, 2001; Vol. 1.
  20. Bartels C.; Karplus M. Multidimensional adaptive umbrella sampling: Applications to main chain and side chain peptide conformations. J. Comput. Chem. 1997, 18, 1450– 1462. 10.1002/(SICI)1096-987X(199709)18:12<1450::AID-JCC3>3.0.CO;2-I. · DOI
  21. Kumar S.; Rosenberg J. M.; Bouzida D.; Swendsen R. H.; Kollman P. A. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 1992, 13, 1011– 1021. 10.1002/jcc.540130812. · DOI · Paper page
  22. Roux B. The calculation of the potential of mean force using computer simulations. Comput. Phys. Commun. 1995, 91, 275– 282. 10.1016/0010-4655(95)00053-I. · DOI
  23. Zhu F.; Hummer G. Convergence and error estimation in free energy calculations using the weighted histogram analysis method. J. Comput. Chem. 2012, 33, 453– 465. 10.1002/jcc.21989. 22109354 PMC3271861 · DOI
  24. Kästner J.; Thiel W. Analysis of the statistical error in umbrella sampling simulations by umbrella integration. J. Chem. Phys. 2006, 124, 234106 10.1063/1.2206775. 16821906 · DOI
  25. Park S.; Im W. Theory of Adaptive Optimization for Umbrella Sampling. J. Chem. Theory Comput. 2014, 10, 2719– 2728. 10.1021/ct500504g. 25018672 PMC4089916 · DOI
  26. Kästner J. Umbrella sampling. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2011, 1, 932– 942. 10.1002/wcms.66. · DOI
  27. Jo S.; Suh D.; He Z.; Chipot C.; Roux B. Leveraging the Information from Markov State Models To Improve the Convergence of Umbrella Sampling Simulations. J. Phys. Chem. B 2016, 120, 8733– 8742. 10.1021/acs.jpcb.6b05125. 27409349 · DOI
  28. Lin J. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 1991, 37, 145– 151. 10.1109/18.61115. · DOI
  29. Straatsma T. P.; McCammon J. A. Multiconfiguration thermodynamic integration. J. Chem. Phys. 1991, 95, 1175– 1188. 10.1063/1.461148. · DOI
  30. Demuynck R.; Wieme J.; Rogge S. M. J.; Dedecker K. D.; Vanduyfhuys L.; Waroquier M.; Van Speybroeck V. Protocol for Identifying Accurate Collective Variables in Enhanced Molecular Dynamics Simulations for the Description of Structural Transformations in Flexible Metal–Organic Frameworks. J. Chem. Theory Comput. 2018, 14, 5511– 5526. 10.1021/acs.jctc.8b00725. 30336016 PMC6236469 · DOI
  31. Sugita Y.; Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 1999, 314, 141– 151. 10.1016/S0009-2614(99)01123-9. · DOI
  32. Sugita Y.; Kitao A.; Okamoto Y. Multidimensional replica-exchange method for free-energy calculations. J. Chem. Phys. 2000, 113, 6042– 6051. 10.1063/1.1308516. · DOI
  33. Shannon C. Communication in the Presence of Noise. Proc. IRE 1949, 37, 10– 21. 10.1109/JRPROC.1949.232969. · DOI · Paper page
  34. Bocus M.; Goeminne R.; Lamaire A.; Cools-Ceuppens M.; Verstraelen T.; Van Speybroeck V. Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics. Nat. Commun. 2023, 14, 1008 10.1038/s41467-023-36666-y. 36823162 PMC9950054 · DOI · Paper page
  35. Lukose B.; Kuc A.; Frenzel J.; Heine T. On the reticular construction concept of covalent organic frameworks. Beilstein J. Nanotechnol. 2010, 1, 60– 70. 10.3762/bjnano.1.8. 21977395 PMC3045923 · DOI
  36. Lukose B.; Kuc A.; Heine T. The structure of layered covalent-organic frameworks. Chemistry 2011, 17, 2388– 2392. 10.1002/chem.201001290. 21259346 · DOI
  37. Koo B. T.; Dichtel W. R.; Clancy P. A classification scheme for the stacking of two-dimensional boronate ester-linked covalent organic frameworks. J. Mater. Chem. 2012, 22, 17460– 17469. 10.1039/c2jm32009f. · DOI
  38. Pütz A. M.; Terban M. W.; Bette S.; Haase F.; Dinnebier R. E.; Lotsch B. V. Total scattering reveals the hidden stacking disorder in a 2D covalent organic framework. Chem. Sci. 2020, 11, 12647– 12654. 10.1039/D0SC03048A. 34094458 PMC8163241 · DOI
  39. Winkler C.; Kamencek T.; Zojer E. Understanding the origin of serrated stacking motifs in planar two-dimensional covalent organic frameworks. Nanoscale 2021, 13, 9339– 9353. 10.1039/D1NR01047F. 33998630 · DOI
  40. Borgmans S.; Rogge S. M. J.; De Vos J. S.; Stevens C. V.; Van Der Voort P.; Van Speybroeck V. Quantifying the Likelihood of Structural Models through a Dynamically Enhanced Powder X-Ray Diffraction Protocol. Angew. Chem., Int. Ed. 2021, 60, 8913– 8922. 10.1002/anie.202017153. PMC8048908 33493379 · DOI
  41. Zhang Y.; Položij M.; Heine T. Statistical Representation of Stacking Disorder in Layered Covalent Organic Frameworks. Chem. Mater. 2022, 34, 2376– 2381. 10.1021/acs.chemmater.1c04365. · DOI
  42. Rawat K. S.; Borgmans S.; Braeckevelt T.; Stevens C. V.; Van Der Voort P.; Van Speybroeck V. How the Layer Alignment in Two-Dimensional Nanoporous Covalent Organic Frameworks Impacts Its Electronic Properties. ACS Appl. Nano Mater. 2022, 5, 14377– 14387. 10.1021/acsanm.2c02647. · DOI

Cited by

Sorted by citations

Authors
Borgmans, S., Rogge, S. M. J., Vanduyfhuys, L., & Van Speybroeck, V.
Affiliations
  • Ghent University (BE)
  • Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, 9052 Zwijnaarde, Belgium
Corresponding author
Rogge, Sven M. J.
ORCID
Venue
Journal of Chemical Theory and Computation
Publisher
American Chemical Society (ACS)
Published
2023-12-12
Volume
19
Issue
24
Pages
9032-9048
ISSN
  • 1549-9618 (ISSN-L)
  • 1549-9626
DOI
10.1021/acs.jctc.3c01028
PMC
PMC10753773
OpenAlex
W4389625430
Cited by
8
References
1
Citations by year
2026: 4 · 2024: 4
FWCI
1.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Chemical Theory and Computation (publishedVersion, cc-by-nc-nd)
  • ChemRxiv (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by-nc-nd)
Funders
  • Vlaams Supercomputer Centrum
  • Vlaamse regering
  • Fonds Wetenschappelijk Onderzoek (12T3522N)
  • Bijzonder Onderzoeksfonds UGent (GOA010-17)
Topics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
free energy landscape, mesh refinement, umbrella sampling, weighted histogram analysis method, proton hopping, zeolites, 2D covalent organic frameworks, enhanced sampling, Python library, activation barrier

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review