Application to large systems: general discussion

Althorpe, Stuart C.; Angulo, Gonzalo; Astumian, Raymond Dean; Beniwal, Vijay; Bolhuis, Peter G.; Brandão, João; Ellis, John; Fang, Wei; Glowacki, David R.; Hammes‐Schiffer, Sharon; Jónsson, Hannes; Lelièvre, Tony; Makri, Nancy; Manolopoulos, David E.; Mebel, Alexander M.; Menzl, Georg; Miller, Thomas F.; Parrinello, Michele; Piaggi, Pablo M.; Pollak, Eli; Chowdhury, Priyadarshi Roy; Sanz, Eduardo; Shalashilin, Dmitry; Skúlason, Egill; Spezia, Riccardo; Taraphder, Srabani

doi:10.1039/c6fd90076c

Cited by 4 publications

(2 citation statements)

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“…activates its reaction with 1 to 19, the latter two of which react further to 33 In a practical setup, one would conduct the kinetic analysis presented above not only at the end but rather repeatedly during the exploration as many of the intermediates and transition states may be kinetically irrelevant. The number of such redundant states potentially grows superlinearly with the size of the network since every vertex that can only be reached via irrelevant channels will be irrelevant as well.…”

Section: Exemplary Kinetx Workflowmentioning

confidence: 99%

“…As an alternative to ensembles of electronic structure models, advanced machine learning methods (e.g., Gaussian process regression or Bayesian neural network regression) 29 could be employed for the accurate estimation of uncertainty-equipped model parameters. The state of the art in reaction rate theory has recently been presented and discussed at the 2016 Faraday Discussion on Reaction Rate Theory, [30][31][32][33] where we have already presented the principle workflow to arrive at first-principles free energies equipped with correlated uncertainties at the example of a small model network of the formose reaction. 26 We showed that the propagation of correlated uncertainty in activation free energies to time-dependent species concentrations can yield striking variances in equilibration times.…”

Section: Introductionmentioning

confidence: 99%

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Mechanism Deduction from Noisy Chemical Reaction Networks

Proppe

Reiher

2018

J. Chem. Theory Comput.

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We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KiNetX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KiNetX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data.

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Section: Exemplary Kinetx Workflowmentioning

confidence: 99%