scholarly journals Integrated tempering enhanced sampling method as the infinite switching limit of simulated tempering

2018 ◽  
Vol 149 (8) ◽  
pp. 084114 ◽  
Author(s):  
Zhiyi You ◽  
Liying Li ◽  
Jianfeng Lu ◽  
Hao Ge
Keyword(s):  
Sampling Method ◽  
Enhanced Sampling ◽  
Simulated Tempering

scholarly journals Neural networks-based variationally enhanced sampling

2019 ◽  
Vol 116 (36) ◽  
pp. 17641-17647 ◽  
Author(s):  
Luigi Bonati ◽  
Yue-Yu Zhang ◽  
Michele Parrinello
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Atomistic Simulation ◽  
Sampling Method ◽  
Machine Learning Techniques ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Learning Techniques ◽  
Minimization Technique ◽  
Energy Surfaces

Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.

scholarly journals A variational approach to nucleation simulation

2016 ◽  
Vol 195 ◽  
pp. 557-568 ◽  
Author(s):  
Pablo M. Piaggi ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Molecular Dynamics ◽  
Computer Simulation ◽  
Free Energy ◽  
Variational Approach ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Technical Problems ◽  
Lennard Jones ◽  
Dynamics Simulations

We study by computer simulation the nucleation of a supersaturated Lennard-Jones vapor into the liquid phase. The large free energy barriers to transition make the time scale of this process impossible to study by ordinary molecular dynamics simulations. Therefore we use a recently developed enhanced sampling method [Valsson and Parrinello, Phys. Rev. Lett.113, 090601 (2014)] based on the variational determination of a bias potential. We differ from previous applications of this method in that the bias is constructed on the basis of the physical model provided by the classical theory of nucleation. We examine the technical problems associated with this approach. Our results are very satisfactory and will pave the way for calculating the nucleation rates in many systems.

scholarly journals Deep learning the slow modes for rare events sampling

2021 ◽  
Vol 118 (44) ◽  
pp. e2113533118
Author(s):  
Luigi Bonati ◽  
GiovanniMaria Piccini ◽  
Michele Parrinello
Keyword(s):  
Sampling Method ◽  
Rare Events ◽  
Conformational Transition ◽  
Transfer Operator ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Long Time ◽  
Computational Resources

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system’s modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.

scholarly journals Exploring Protocols to Build Reservoirs to Accelerate Replica Exchange MD Simulations

2020 ◽  
Author(s):  
koushik kasavajhala ◽  
kenneth lam ◽  
Carlos Simmerling
Keyword(s):  
Amino Acids ◽  
Sampling Method ◽  
Convergence Rates ◽  
Md Simulations ◽  
Replica Exchange ◽  
Conformational Sampling ◽  
Enhanced Sampling ◽  
Replica Exchange Molecular Dynamics ◽  
The Past ◽  
Biomolecular Simulations

Replica Exchange Molecular Dynamics (REMD) is a widely used enhanced sampling method for accelerating biomolecular simulations. During the past two decades, several variants of REMD have been developed to further improve the rate of conformational sampling of REMD. One such variant, Reservoir REMD (RREMD), was shown to improve the rate of conformational sampling by around 5-20x. Despite the significant increase in sampling speed, RREMD methods have not been widely used due to the difficulties in building the reservoir and also due to the code not being available on the GPUs.<br><br>In this work, we ported the AMBER RREMD code onto GPUs making it 20x faster than the CPU code. Then, we explored protocols for building Boltzmann-weighted reservoirs as well as non-Boltzmann reservoirs, and tested how each choice affects the accuracy of the resulting RREMD simulations. We show that, using the recommended protocols outlined here, RREMD simulations can accurately reproduce Boltzmann-weighted ensembles obtained by much more expensive conventional REMD simulations, with at least 15x faster convergence rates even for larger proteins (>50 amino acids) compared to conventional REMD.

Selective enhanced sampling in dihedral energy facilitates overcoming the dihedral energy increase in protein folding and accelerates the searching for protein native structure

2019 ◽  
Vol 21 (20) ◽  
pp. 10423-10435 ◽  
Author(s):  
Qiang Shao ◽  
Lijiang Yang ◽  
Weiliang Zhu
Keyword(s):  
Protein Folding ◽  
Sampling Method ◽  
Folding Pathway ◽  
Enhanced Sampling ◽  
Native Structure ◽  
Energy Increase ◽  
Folded Structure

A dihedral-energy-based selective enhanced sampling method (D-SITSMD) is presented with improved capabilities for searching a protein's natively folded structure and for providing the underlying folding pathway.

scholarly journals 2P128 Structure analysis of DNA mini hairpin using locally enhanced sampling method

2005 ◽  
Vol 45 (supplement) ◽  
pp. S151
Author(s):  
S. Yamasaki ◽  
S. Nakamura ◽  
T. Terada ◽  
K. Shimizu
Keyword(s):  
Structure Analysis ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Locally Enhanced Sampling

scholarly journals Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Matthias Morzfeld
Keyword(s):  
Monte Carlo ◽  
Numerical Experiments ◽  
High Probability ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Integral Control ◽  
Localization And Mapping ◽  
Monte Carlo Localization ◽  
Estimation And Control ◽  
And Control

Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the performance of algorithms that rely on Monte Carlo (MC) methods. Here the applicability and usefulness of implicit sampling for improving the performance of MC methods in estimation and control is explored, and implicit sampling based algorithms for stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM) are presented. The algorithms are tested in numerical experiments where it is found that fewer samples are required if implicit sampling is used, and that the overall runtimes of the algorithms are reduced.

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