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Author |
Bal, K.M. |
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Title |
Reweighted Jarzynski sampling : acceleration of rare events and free energy calculation with a bias potential learned from nonequilibrium work |
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A1 Journal article |
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Year  |
2021 |
Publication |
Journal Of Chemical Theory And Computation |
Abbreviated Journal |
J Chem Theory Comput |
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Volume |
17 |
Issue |
11 |
Pages |
6766-6774 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Abstract |
We introduce a simple enhanced sampling approach for the calculation of free energy differences and barriers along a one-dimensional reaction coordinate. First, a small number of short nonequilibrium simulations are carried out along the reaction coordinate, and the Jarzynski equality is used to learn an approximate free energy surface from the nonequilibrium work distribution. This free energy estimate is represented in a compact form as an artificial neural network and used as an external bias potential to accelerate rare events in a subsequent molecular dynamics simulation. The final free energy estimate is then obtained by reweighting the equilibrium probability distribution of the reaction coordinate sampled under the influence of the external bias. We apply our reweighted Jarzynski sampling recipe to four processes of varying scales and complexities.spanning chemical reaction in the gas phase, pair association in solution, and droplet nucleation in supersaturated vapor. In all cases, we find reweighted Jarzynski sampling to be a very efficient strategy, resulting in rapid convergence of the free energy to high precision. |
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Wos |
000718183600008 |
Publication Date |
2021-10-29 |
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ISSN |
1549-9618 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
5.245 |
Times cited |
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Open Access |
OpenAccess |
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Approved |
Most recent IF: 5.245 |
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Call Number |
UA @ admin @ c:irua:184676 |
Serial |
8479 |
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Author |
Bal, K.M.; Neyts, E.C. |
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Title |
Merging Metadynamics into Hyperdynamics: Accelerated Molecular Simulations Reaching Time Scales from Microseconds to Seconds |
Type |
A1 Journal article |
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Year |
2015 |
Publication |
Journal of chemical theory and computation |
Abbreviated Journal |
J Chem Theory Comput |
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Volume |
11 |
Issue |
11 |
Pages |
4545-4554 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Abstract |
The hyperdynamics method is a powerful tool to simulate slow processes at the atomic level. However, the construction of an optimal hyperdynamics potential is a task that is far from trivial. Here, we propose a generally applicable implementation of the hyperdynamics algorithm, borrowing two concepts from metadynamics. First, the use of a collective variable (CV) to represent the accelerated dynamics gives the method a very large flexibility and simplicity. Second, a metadynamics procedure can be used to construct a suitable history-dependent bias potential on-the-fly, effectively turning the algorithm into a self-learning accelerated molecular dynamics method. This collective variable-driven hyperdynamics (CVHD) method has a modular design: both the local system properties on which the bias is based, as well as the characteristics of the biasing method itself, can be chosen to match the needs of the considered system. As a result, system-specific details are abstracted from the biasing algorithm itself, making it extremely versatile and transparent. The method is tested on three model systems: diffusion on the Cu(001) surface and nickel-catalyzed methane decomposition, as examples of reactive processes with a bond-length-based CV, and the folding of a long polymer-like chain, using a set of dihedral angles as a CV. Boost factors up to 109, corresponding to a time scale of seconds, could be obtained while still accurately reproducing correct dynamics. |
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Wos |
000362921700004 |
Publication Date |
2015-09-02 |
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ISSN |
1549-9618 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
5.245 |
Times cited |
41 |
Open Access |
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Notes |
K.M.B. is funded as Ph.D. fellow (aspirant) of the FWOFlanders (Fund for Scientific Research-Flanders), Grant No. 11 V8915N. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center) and the HPC infrastructure of the University of Antwerp (CalcUA), funded by the Hercules Foundation and the Flemish Government−Department EWI. |
Approved |
Most recent IF: 5.245; 2015 IF: 5.498 |
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Call Number |
c:irua:128183 |
Serial |
3991 |
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Author |
Neyts, E.C.; Thijsse, B.J.; Mees, M.J.; Bal, K.M.; Pourtois, G. |
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Title |
Establishing uniform acceptance in force biased Monte Carlo simulations |
Type |
A1 Journal article |
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Year |
2012 |
Publication |
Journal of chemical theory and computation |
Abbreviated Journal |
J Chem Theory Comput |
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8 |
Issue |
6 |
Pages |
1865-1869 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Abstract |
Uniform acceptance force biased Monte Carlo (UFMC) simulations have previously been shown to be a powerful tool to simulate atomic scale processes, enabling one to follow the dynamical path during the simulation. In this contribution, we present a simple proof to demonstrate that this uniform acceptance still complies with the condition of detailed balance, on the condition that the characteristic parameter lambda = 1/2 and that the maximum allowed step size is chosen to be sufficiently small. Furthermore, the relation to Metropolis Monte Carlo (MMC) is also established, and it is shown that UFMC reduces to MMC by choosing the characteristic parameter lambda = 0 [Rao, M. et al. Mol. Phys. 1979, 37, 1773]. Finally, a simple example compares the UFMC and MMC methods. |
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Wos |
000305092400002 |
Publication Date |
2012-05-16 |
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ISSN |
1549-9618;1549-9626; |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
5.245 |
Times cited |
20 |
Open Access |
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Notes |
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Approved |
Most recent IF: 5.245; 2012 IF: 5.389 |
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Call Number |
UA @ lucian @ c:irua:99090 |
Serial |
1082 |
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