ACS Publications. Most Trusted. Most Cited. Most Read
Making the Best of a Bad Situation: A Multiscale Approach to Free Energy Calculation

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Chem. Theory Comput. 2019, 15, 4, 2187-2194
    Article

    Making the Best of a Bad Situation: A Multiscale Approach to Free Energy Calculation
    Click to copy article linkArticle link copied!

    • Michele Invernizzi*
      Michele Invernizzi
      Department of Physics, ETH Zurich c/o USI Campus, 6900 Lugano, Switzerland
      Facoltà di Informatica, Instituto di Scienze Computationali, and National Center for Computational Design and Discovery of Novel Materials MARVEL, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
      *E-mail: [email protected]
      More by Michele Invernizzi
      Orcidhttp://orcid.org/0000-0002-3328-6557
    • Michele Parrinello
      Michele Parrinello
      Department of Chemistry and Applied Biosciences, ETH Zurich c/o USI Campus, 6900 Lugano, Switzerland
      Facoltà di Informatica, Instituto di Scienze Computationali, and National Center for Computational Design and Discovery of Novel Materials MARVEL, Università della Svizzera Italiana (USI), Via Giuseppe Buffi 13, CH-6900 Lugano, Switzerland
      More by Michele Parrinello
    Other Access OptionsSupporting Information (1)

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2019, 15, 4, 2187–2194
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.9b00032
    Published March 1, 2019
    Copyright © 2019 American Chemical Society
    Request reuse permissions

    Abstract

    Click to copy section linkSection link copied!
    Abstract Image

    Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space, one is then able to sample efficiently and reconstruct the free energy landscape. In methods such as metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the nonideal situation of a system in which some slow modes are not accelerated. We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.

    Copyright © 2019 American Chemical Society

    Subjects

    what are subjects

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. Add or change your institution or let them know you’d like them to include access.

    Supporting Information

    Click to copy section linkSection link copied!

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jctc.9b00032.

    • Sampling with different CVs; notes on eqs 12 and 11; optimization algorithm; illustrative model; computational details and more results for alanine dipeptide; computational details for sodium (PDF)

    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

    Cited By

    Click to copy section linkSection link copied!
    Citation Statements
    Explore this article's citation statements on scite.ai
    powered by  Scite

    This article is cited by 25 publications.

    1. Jonathan R. Church, Ofir Blumer, Tommer D. Keidar, Leo Ploutno, Shlomi Reuveni, Barak Hirshberg. Accelerating Molecular Dynamics through Informed Resetting. Journal of Chemical Theory and Computation 2025, 21 (2) , 605-613. https://doi.org/10.1021/acs.jctc.4c01238
    2. Dhiman Ray, Valerio Rizzi. Enhanced Sampling with Suboptimal Collective Variables: Reconciling Accuracy and Convergence Speed. Journal of Chemical Theory and Computation 2025, 21 (1) , 58-69. https://doi.org/10.1021/acs.jctc.4c01231
    3. Timur Magsumov, Ilya Ibraev, Igor Sedov. Probing the Conformational Ensemble of the Amyloid Beta 16–22 Fragment with Parallel-Bias Metadynamics. The Journal of Physical Chemistry B 2024, 128 (50) , 12333-12347. https://doi.org/10.1021/acs.jpcb.4c04919
    4. Antoniu Bjola, Matteo Salvalaglio. Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration. Journal of Chemical Theory and Computation 2024, 20 (13) , 5418-5427. https://doi.org/10.1021/acs.jctc.4c00091
    5. Ofir Blumer, Shlomi Reuveni, Barak Hirshberg. Short-Time Infrequent Metadynamics for Improved Kinetics Inference. Journal of Chemical Theory and Computation 2024, 20 (9) , 3484-3491. https://doi.org/10.1021/acs.jctc.4c00170
    6. Oren Elishav, Roy Podgaetsky, Olga Meikler, Barak Hirshberg. Collective Variables for Conformational Polymorphism in Molecular Crystals. The Journal of Physical Chemistry Letters 2023, 14 (4) , 971-976. https://doi.org/10.1021/acs.jpclett.2c03491
    7. Michele Invernizzi, Andreas Krämer, Cecilia Clementi, Frank Noé. Skipping the Replica Exchange Ladder with Normalizing Flows. The Journal of Physical Chemistry Letters 2022, 13 (50) , 11643-11649. https://doi.org/10.1021/acs.jpclett.2c03327
    8. Ofir Blumer, Shlomi Reuveni, Barak Hirshberg. Stochastic Resetting for Enhanced Sampling. The Journal of Physical Chemistry Letters 2022, 13 (48) , 11230-11236. https://doi.org/10.1021/acs.jpclett.2c03055
    9. Dhiman Ray, Narjes Ansari, Valerio Rizzi, Michele Invernizzi, Michele Parrinello. Rare Event Kinetics from Adaptive Bias Enhanced Sampling. Journal of Chemical Theory and Computation 2022, 18 (11) , 6500-6509. https://doi.org/10.1021/acs.jctc.2c00806
    10. Pietro Novelli, Luigi Bonati, Massimiliano Pontil, Michele Parrinello. Characterizing Metastable States with the Help of Machine Learning. Journal of Chemical Theory and Computation 2022, 18 (9) , 5195-5202. https://doi.org/10.1021/acs.jctc.2c00393
    11. Benjamin Pampel, Omar Valsson. Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials. Journal of Chemical Theory and Computation 2022, 18 (7) , 4127-4141. https://doi.org/10.1021/acs.jctc.2c00197
    12. Michele Invernizzi, Michele Parrinello. Exploration vs Convergence Speed in Adaptive-Bias Enhanced Sampling. Journal of Chemical Theory and Computation 2022, 18 (6) , 3988-3996. https://doi.org/10.1021/acs.jctc.2c00152
    13. Kristof M. Bal. Reweighted Jarzynski Sampling: Acceleration of Rare Events and Free Energy Calculation with a Bias Potential Learned from Nonequilibrium Work. Journal of Chemical Theory and Computation 2021, 17 (11) , 6766-6774. https://doi.org/10.1021/acs.jctc.1c00574
    14. Jayashrita Debnath, Michele Parrinello. Gaussian Mixture-Based Enhanced Sampling for Statics and Dynamics. The Journal of Physical Chemistry Letters 2020, 11 (13) , 5076-5080. https://doi.org/10.1021/acs.jpclett.0c01125
    15. Michele Invernizzi, Michele Parrinello. Rethinking Metadynamics: From Bias Potentials to Probability Distributions. The Journal of Physical Chemistry Letters 2020, 11 (7) , 2731-2736. https://doi.org/10.1021/acs.jpclett.0c00497
    16. Sahithya Sridharan Iyer, Jiangbo Wu, Thomas D. Pollard, Gregory A. Voth. Molecular mechanism of Arp2/3 complex activation by nucleation-promoting factors and an actin monomer. Proceedings of the National Academy of Sciences 2025, 122 (10) https://doi.org/10.1073/pnas.2421467122
    17. Martijn P. Bemelmans, Zoe Cournia, Kelly L. Damm-Ganamet, Francesco L. Gervasio, Vineet Pande. Computational advances in discovering cryptic pockets for drug discovery. Current Opinion in Structural Biology 2025, 90 , 102975. https://doi.org/10.1016/j.sbi.2024.102975
    18. Ofir Blumer, Shlomi Reuveni, Barak Hirshberg. Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations. Nature Communications 2024, 15 (1) https://doi.org/10.1038/s41467-023-44528-w
    19. Carsten Hartmann, Lorenz Richter. Nonasymptotic Bounds for Suboptimal Importance Sampling. SIAM/ASA Journal on Uncertainty Quantification 2024, 12 (2) , 309-346. https://doi.org/10.1137/21M1427760
    20. Abhinav Gupta, Shivani Verma, Ramsha Javed, Suraj Sudhakar, Saurabh Srivastava, Nisanth N. Nair. Exploration of high dimensional free energy landscapes by a combination of temperature‐accelerated sliced sampling and parallel biasing. Journal of Computational Chemistry 2022, 43 (17) , 1186-1200. https://doi.org/10.1002/jcc.26882
    21. Federica Lodesani, Maria Cristina Menziani, Shingo Urata, Alfonso Pedone. Biasing crystallization in fused silica: An assessment of optimal metadynamics parameters. The Journal of Chemical Physics 2022, 156 (19) https://doi.org/10.1063/5.0089183
    22. Luigi Bonati, GiovanniMaria Piccini, Michele Parrinello. Deep learning the slow modes for rare events sampling. Proceedings of the National Academy of Sciences 2021, 118 (44) https://doi.org/10.1073/pnas.2113533118
    23. Tarak Karmakar, Michele Invernizzi, Valerio Rizzi, Michele Parrinello. Collective variables for the study of crystallisation. Molecular Physics 2021, 119 (19-20) https://doi.org/10.1080/00268976.2021.1893848
    24. Michele Invernizzi, Pablo M. Piaggi, Michele Parrinello. Unified Approach to Enhanced Sampling. Physical Review X 2020, 10 (4) https://doi.org/10.1103/PhysRevX.10.041034
    25. Luigi Bonati, Yue-Yu Zhang, Michele Parrinello. Neural networks-based variationally enhanced sampling. Proceedings of the National Academy of Sciences 2019, 116 (36) , 17641-17647. https://doi.org/10.1073/pnas.1907975116
    Download PDF
    Go to Journal of Chemical Theory and Computation
    Get e-Alerts
    Get e-Alerts

    Journal of Chemical Theory and Computation

    Cite this: J. Chem. Theory Comput. 2019, 15, 4, 2187–2194
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jctc.9b00032
    Published March 1, 2019
    Copyright © 2019 American Chemical Society
    Request reuse permissions

    Article Views

    2103

    Altmetric

    -

    Citations

    25
    Learn about these metrics

    Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

    Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.