Search

Your search keyword '"Roet, Sander"' showing total 17 results
Start Over Author "Roet, Sander"
17 results on '"Roet, Sander"'

1. Exchanging replicas with unequal cost, infinitely and permanently

Author

Roet, Sander, Zhang, Daniel T., and van Erp, Titus S.

Subjects
Physics - Computational Physics, Condensed Matter - Statistical Mechanics, Physics - Chemical Physics
Abstract

We developed a replica exchange method that is effectively parallelizable even if the computational cost of the Monte Carlo moves in the parallel replicas are considerably different, for instance, because the replicas run on different type of processor units or because of the algorithmic complexity. To prove detailed-balance, we make a paradigm shift from the common conceptual viewpoint in which the set of parallel replicas represents a high-dimensional superstate, to an ensemble based criterion in which the other ensembles represent an environment that might or might not participate in the Monte Carlo move. In addition, based on a recent algorithm for computing permanents, we effectively increase the exchange rate to infinite without the steep factorial scaling as function of the number of replicas. We illustrate the effectiveness of the replica exchange methodology by combining it with a quantitative path sampling method, replica exchange transition interface sampling (RETIS), in which the costs for a Monte Carlo move can vary enormously as paths in a RETIS algorithm do not have the same length and the average path lengths tend to vary considerably for the different path ensembles that run in parallel. This combination, coined $\infty$RETIS, was tested on three model systems.

Published
2022
Full Text
View/download PDF

2. Chemistrees: data driven identification of reaction pathways via machine learning

Author

Roet, Sander, Daub, Christopher David, and Riccardi, Enrico

Subjects
Physics - Chemical Physics, Physics - Computational Physics
Abstract

We propose a supervised machine learning algorithm, decision trees, to analyze molecular dynamics output. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-based algorithm aims to identify such features in an approach which is unbiased by human "chemical intuition". We demonstrate the method by analyzing proton exchange reactions in formic acid (FA) solvated in small water clusters. The simulations were performed with ab initio molecular dynamics combined with a method for generating rare events, specifically path sampling. Our machine learning analysis identified mechanistic descriptions of the proton transfer reaction for the different water clusters.

Published
2021
Full Text
View/download PDF

4. PyRETIS 3: Conquering rare and slow events without boundaries.

Author

Vervust, Wouter, Zhang, Daniel T., Ghysels, An, Roet, Sander, van Erp, Titus S., and Riccardi, Enrico

Subjects
METASTABLE states, MODULAR construction, MACHINE learning, PYTHON programming language, ALGORITHMS, BIOCHEMICAL substrates
Abstract

We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user‐friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long‐lived metastable states and transitions with unbounded reactant and product states. Additionally, the post‐analysis software PyVisa is now embedded in the main code, allowing fast use of machine‐learning algorithms for clustering and visualising collective variables in the simulation data. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

11. Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas

Author

Roet, Sander, Hooft, Ferry, Bolhuis, Peter G., Swenson, David W.H., and Vreede, Jocelyne

Subjects
neoplasms, digestive system diseases
Abstract

The GTPase KRas is a signaling protein in networks for cell differentiation, growth, and division. KRas mutations can prolong activation of these networks, resulting in tumor formation. When active, KRas tightly binds GTP. Several oncogenic mutations affect the conversion between this rigid state and inactive, more flexible states. Detailed understanding of these transitions may provide valuable insights into how mutations affect KRas. Path sampling simulations, which focus on transitions, show KRas visiting several states, which are the same for wild type and the oncogenic mutant Q61L. Large differences occur when converting between these states, indicating the dramatic effect of the Q61L mutation on KRas dynamics. For Q61L a route to the flexible state is inaccessible, thus shifting the equilibrium to more rigid states. Our methodology presents a novel way to predict dynamical effects of KRas mutations, which may aid in identifying potential therapeutic targets.Author summaryCancer cells frequently contain mutations in the protein KRas. However, KRas is a challenging target for anti-cancer drugs, in part because the dynamic behavior of flexible regions in the protein is difficult to characterize experimentally, and occurs on timescales that are too long for straightforward molecular dynamics simulations. We have used path sampling, an advanced simulation technique that overcomes long timescales, to obtain atomistic insight into the dynamics of KRas. Comparing the oncogenic mutant Q61L to the wild type revealed that the mutation closes off one transition channel for deactivating KRas. Our approach opens up the way for predicting the dynamical effects of mutations in KRas, which may aid in identifying potential therapeutic targets.

Published
2020

14. Classical Md E-Cam Modules Ii

Author

Swenson, David W. H., Jony Castagna, Cabriolu, Raffaela, Maslechko, Anastasiia, ClemensMoritz, Roet, Sander, Singraber, Andreas, and Dellago, Christoph

Abstract

9 software modules in classical Molecular Dynamics delivered to the E-CAM repository in the area of statistical and machine learning tools for the analysis of rare events, and their documentation.

Published
2017
Full Text
View/download PDF

15. Chemistrees: Data-Driven Identification of Reaction Pathways viaMachine Learning

Author

Roet, Sander, Daub, Christopher D., and Riccardi, Enrico

Abstract

We propose to analyze molecular dynamics (MD) output viaa supervised machine learning (ML) algorithm, the decision tree. The approach aims to identify the predominant geometric features which correlate with trajectories that transition between two arbitrarily defined states. The data-driven algorithm aims to identify these features without the bias of human “chemical intuition”. We demonstrate the method by analyzing the proton exchange reactions in formic acid solvated in small water clusters. The simulations were performed with ab initioMD combined with a method to efficiently sample the rare event, path sampling. Our ML analysis identified relevant geometric variables involved in the proton transfer reaction and how they may change as the number of solvating water molecules changes.

Published
2021
Full Text
View/download PDF

16. PyRETIS 2: An improbability drive for rare events.

Author

Riccardi, Enrico, Lervik, Anders, Roet, Sander, Aarøen, Ola, and Erp, Titus S.

Subjects
USER interfaces, COMPUTATIONAL chemistry, MOLECULAR dynamics, PYTHON programming language, SAMPLING methods, PERIODICAL publishing
Abstract

The algorithmic development in the field of path sampling has made tremendous progress in recent years. Although the original transition path sampling method was mostly used as a qualitative tool to sample reaction paths, the more recent family of interface‐based path sampling methods has paved the way for more quantitative rate calculation studies. Of the exact methods, the replica exchange transition interface sampling (RETIS) method is the most efficient, but rather difficult to implement. This has been the main motivation to develop the open‐source Python‐based computer library PyRETIS that was released in 2017. PyRETIS is designed to be easily interfaced with any molecular dynamics (MD) package using either classical or ab initio MD. In this study, we report on the principles and the software enhancements that are now included in PyRETIS 2, as well as the recent developments on the user interface, improvements of the efficiency via the implementation of new shooting moves, easier initialization procedures, analysis methods, and supported interfaced software. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results