Fundamentals: general discussion

Althorpe, Stuart C.; Beniwal, Vijay; Bolhuis, Peter G.; Brandão, João; Clary, David C.; Ellis, John; Fang, Wei; Glowacki, David R.; Jónsson, Hannes; Kästner, Johannes; Makri, Nancy; Manolopoulos, David E.; McKemmish, Laura K.; Menzl, Georg; Miller, Thomas F.; Miller, William H.; Pollak, E W; Rampino, Sergio; Richardson, Jeremy O.; Richter, Martin; Chowdhury, Priyadarshi Roy; Shalashilin, Dmitry; Tennyson, Jonathan; Welsch, R

doi:10.1039/c6fd90077a

Cited by 2 publications

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“…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%

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: Introductionmentioning

confidence: 99%

Mechanism Deduction from Noisy Chemical Reaction Networks

Proppe

Reiher

2018

J. Chem. Theory Comput.

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“…These subtle features of the PES leave their footprints in the dynamics and kinetics of formation of

{normalC}_{2}^{+}

from C + CH + , and are of invaluable help in the analysis and interpretation of the outcome of the related calculations. The interested reader is referred to the previously cited works for a more detailed discussion.…”

Section: Case Studiesmentioning

confidence: 99%

Chemical promenades: Exploring potential‐energy surfaces with immersive virtual reality

et al. 2020

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The virtual-reality framework AVATAR (Advanced Virtual Approach to Topological Analysis of Reactivity) for the immersive exploration of potential-energy landscapes is presented. AVATAR is based on modern consumer-grade virtual-reality technology and builds on two key concepts: (a) the reduction of the dimensionality of the potential-energy surface to two process-tailored, physically meaningful generalized coordinates, and (b) the analogy between the evolution of a chemical process and a pathway through valleys (potential wells) and mountain passes (saddle points) of the associated potential energy landscape. Examples including the discovery of competitive reaction paths in simple A + BC collisional systems and the interconversion between conformers in ring-puckering motions of flexible rings highlight the innovation potential that augmented and virtual reality convey for teaching, training, and supporting research in chemistry. K E Y W O R D S atom diatom reactions, immersive virtual reality, potential energy surface, ring puckering motions 1 | INTRODUCTION As well known, rigorous simulations of molecular processes should be based on the solution of the Schrödinger equation for the wavefunction of the involved nuclei and electrons, which is usually cast in its nonrelativistic time-independent form with a Hamiltonian including a kinetic term for the electronsT e , a kinetic term for the nucleiT N , and an interaction-potential term V (r, R), with r and R being the set of spatial coordinates of electrons and nuclei, respectively.This equation is seldom solved as is, due to the associated mathematical difficulties and computational cost. More commonly, on the grounds that the nuclei are much heavier than the electrons, the Born-Oppenheimer approximation [1] is adopted and the problem is broken down in two separate problems, one for the motion of the electrons at a given nuclear geometry:T

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Fundamentals: general discussion

Cited by 2 publications

References 0 publications

Mechanism Deduction from Noisy Chemical Reaction Networks

Mechanism Deduction from Noisy Chemical Reaction Networks

Chemical promenades: Exploring potential‐energy surfaces with immersive virtual reality

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