The Experts below are selected from a list of 41823 Experts worldwide ranked by ideXlab platform
Donald G Truhlar - One of the best experts on this subject based on the ideXlab platform.
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erratum army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method j chem phys 120 3586 2004
Journal of Chemical Physics, 2016Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:Erratum: “Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method” [J. Chem. Phys. 120, 3586 (2004)] Shikha Nangia, Ahren W. Jasper, Thomas F. Miller III, and Donald G. Truhlara) Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, USA (Received 23 February 2016; accepted 10 March 2016; published online 6 April 2016)
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army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method
Journal of Chemical Physics, 2004Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom–diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10^–8. For such low-probability events the anteater sampling scheme requires a large number of trajectories (~10^10) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10^5 trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the Bootstrap Method, which is validated for use with the army ants algorithm.
Shikha Nangia - One of the best experts on this subject based on the ideXlab platform.
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erratum army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method j chem phys 120 3586 2004
Journal of Chemical Physics, 2016Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:Erratum: “Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method” [J. Chem. Phys. 120, 3586 (2004)] Shikha Nangia, Ahren W. Jasper, Thomas F. Miller III, and Donald G. Truhlara) Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, USA (Received 23 February 2016; accepted 10 March 2016; published online 6 April 2016)
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army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method
Journal of Chemical Physics, 2004Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom–diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10^–8. For such low-probability events the anteater sampling scheme requires a large number of trajectories (~10^10) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10^5 trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the Bootstrap Method, which is validated for use with the army ants algorithm.
Ahren W Jasper - One of the best experts on this subject based on the ideXlab platform.
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erratum army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method j chem phys 120 3586 2004
Journal of Chemical Physics, 2016Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:Erratum: “Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method” [J. Chem. Phys. 120, 3586 (2004)] Shikha Nangia, Ahren W. Jasper, Thomas F. Miller III, and Donald G. Truhlara) Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, USA (Received 23 February 2016; accepted 10 March 2016; published online 6 April 2016)
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army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method
Journal of Chemical Physics, 2004Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom–diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10^–8. For such low-probability events the anteater sampling scheme requires a large number of trajectories (~10^10) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10^5 trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the Bootstrap Method, which is validated for use with the army ants algorithm.
Thomas F Miller - One of the best experts on this subject based on the ideXlab platform.
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erratum army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method j chem phys 120 3586 2004
Journal of Chemical Physics, 2016Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:Erratum: “Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method” [J. Chem. Phys. 120, 3586 (2004)] Shikha Nangia, Ahren W. Jasper, Thomas F. Miller III, and Donald G. Truhlara) Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, USA (Received 23 February 2016; accepted 10 March 2016; published online 6 April 2016)
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army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the Bootstrap Method
Journal of Chemical Physics, 2004Co-Authors: Shikha Nangia, Ahren W Jasper, Thomas F Miller, Donald G TruhlarAbstract:The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom–diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10^–8. For such low-probability events the anteater sampling scheme requires a large number of trajectories (~10^10) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10^5 trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the Bootstrap Method, which is validated for use with the army ants algorithm.
Peter Hall - One of the best experts on this subject based on the ideXlab platform.
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a simple Bootstrap Method for constructing nonparametric confidence bands for functions
arXiv: Statistics Theory, 2013Co-Authors: Peter Hall, Joel L HorowitzAbstract:Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the Bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing Methods, complicate the process of inference, for example, by requiring the choice of new, unconventional smoothing parameters and, in the case of undersmoothing, producing relatively wide bands. In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to the problem - the fact that the standard Bootstrap bias estimator suffers from relatively high-frequency stochastic error. The high frequency, together with a technique based on quantiles, can be exploited to dampen down the stochastic error term, leading to relatively narrow, simple-to-construct confidence bands.
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a simple Bootstrap Method for constructing nonparametric confidence bands for functions
Annals of Statistics, 2013Co-Authors: Peter Hall, Joel L HorowitzAbstract:Standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the Bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing Methods, complicate the process of inference, for example by requiring the choice of new, unconventional smoothing parameters and, in the case of undersmoothing, producing relatively wide bands. In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to this problem - the fact that the standard Bootstrap bias estimator suffers from relatively high-frequency stochastic error. The high frequency, together with a technique based on quantiles, can be exploited to dampen down the stochastic error term, leading to relatively narrow, simple-to-construct confidence bands.