On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm

Rassy, Elissa El; Delaroque, Aurélie; Sambou, Patrick; Chakravarty, Harish Kumar; Matynia, Alexis

doi:10.1021/acs.jpca.1c02095

Cited by 14 publications

(8 citation statements)

References 40 publications

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“…Traditional methods require ligand- or structure-based designs that are based on the active site and binder of the target molecule [65] . Other molecular generation or de novo design techniques include inverse QSAR10 [25] , [66] , particle swarm optimization, and genetic algorithms [25] , [67] . These methods generate new molecules with specific chemical properties based on the properties of training datasets.…”

Section: De Novo Synthesis and Prediction Of Compound Proper...mentioning

confidence: 99%

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Mathema

Sen

Lamichhane

et al. 2023

Computational and Structural Biotechnology Journal

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Section: De Novo Synthesis and Prediction Of Compound Proper...mentioning

confidence: 99%

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Mathema

Sen

Lamichhane

et al. 2023

Computational and Structural Biotechnology Journal

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“…To this end, we extend the idea of a pc − , basis set and combine it with the particle swarm optimization (PSO) algorithm, a metaheuristic biologically inspired algorithm for a simple and efficient global minimum search of a multivariable function. This algorithm has been successfully applied to several problems in physical chemistry, such as minimum-energy structure search − and kinetic mechanisms . To the best of our knowledge, this paper presents the first PSO application for basis set optimization.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm has been successfully applied to several problems in physical chemistry, such as minimumenergy structure search 76−78 and kinetic mechanisms. 79 To the best of our knowledge, this paper presents the first PSO application for basis set optimization.…”

Section: Introductionmentioning

confidence: 99%

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization

Rueda Espinosa,

Kananenka,

Rusakov

2023

J. Chem. Theory Comput.

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Using the example of astatine, the heaviest naturally occurring halogen whose isotope At-211 has promising medical applications, we propose a new infrastructure for large-scale computational models of heavy elements with strong relativistic effects. In particular, we focus on developing an accurate force field for At– in water based on reliable relativistic density functional theory (DFT) calculations. To ensure the reliability of such calculations, we design novel basis sets for relativistic DFT, via the particle swarm optimization algorithm to optimize the coefficients of the new basis sets and the polarization-consistent basis set idea’s extension to heavy elements to eliminate the basis set error from DFT calculations. The resulting basis sets enable the well-grounded evaluation of relativistic DFT against “gold-standard” CCSD(T) results. Accounting for strong relativistic effects, including spin–orbit interaction, via our redesigned infrastructure, we elucidate a noticeable dissimilarity between At– and I– in halide–water force field parameters, radial distribution functions, diffusion coefficients, and hydration energies. This work establishes the framework for the systematic development of polarization-consistent basis sets for relativistic DFT and accurate force fields for molecular dynamics simulations to be used in large-scale models of complex molecular systems with elements from the bottom of the periodic table, including actinides and even superheavy elements.

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“…This algorithm has been successfully applied to several problems in physical chemistry, such as minimum-energy structure search [74][75][76] and kinetic mechanisms. 77 To the best of our knowledge, this article presents the first PSO application for basis set optimization.…”

Section: Introductionmentioning

confidence: 99%

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization

Espinosa,

Kananenka,

Rusakov

2023

Preprint

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Using the example of astatine, the heaviest naturally occurring halogen whose isotope At-211 has promising medical applications, we propose a new infrastructure for large-scale computational models of heavy elements with strong relativistic effects. In particular, we focus on developing an accurate force field for At– in water based on reliable relativistic DFT calculations. To ensure such calculations’ reliability, we design novel basis sets for relativistic DFT via the polarization-consistent basis set idea’s extension to heavy elements, thus eliminating the basis-set error from DFT calculations. The resulting basis sets enable the well-grounded evaluation of relativistic DFT against “gold-standard” CCSD(T) results. We employ the evolutionary algorithm called particle swarm optimization for the force field and basis set efficient optimization. Accounting for strong relativistic effects, including spin-orbit interaction, via our redesigned infrastructure, we elucidate a noticeable dissimilarity between At– and I– in halide–water force field parameters, radial distribution functions, diffusion coefficients, and hydration energies. This works establishes the framework for the systematic development of polarization-consistent basis sets for relativistic DFT and accurate force fields for molecular dynamics simulations to be used in large-scale models of complex molecular systems with the elements from the bottom of the periodic table, including actinides and even superheavy elements.

show abstract

On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm

Cited by 14 publications

References 40 publications

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization

Novel Computational Chemistry Infrastructure for Simulating Astatide in Water: From Basis Sets to Force Fields Using Particle Swarm Optimization

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