Exploration of Reaction Pathways and Chemical Transformation Networks

Hernández-Lobato, José Miguel; Vaucher, Alain C.; Reiher, Markus

doi:10.1021/acs.jpca.8b10007

Cited by 197 publications

(220 citation statements)

References 261 publications

(426 reference statements)

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“…It can also actively participate in a reaction in such a way that well‐structured intermediates including solvent molecules are formed. Therefore, the accurate theoretical description of chemical processes requires not only the elucidation of all relevant intermediates and elementary reactions (see References ) but also adequate modeling of the reactants’ environment.…”

Section: Introductionmentioning

confidence: 99%

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Systematic microsolvation approach with a cluster‐continuum scheme and conformational sampling

2020

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Solvation is a notoriously difficult and nagging problem for the rigorous theoretical description of chemistry in the liquid phase. Successes and failures of various approaches ranging from implicit solvation modeling through dielectric continuum embedding and microsolvated quantum chemical modeling to explicit molecular dynamics highlight this situation. Here, we focus on quantum chemical microsolvation and discuss an explicit conformational sampling ansatz to make this approach systematic. For this purpose, we introduce an algorithm for rolling and automated microsolvation of solutes. Our protocol takes conformational sampling and rearrangements in the solvent shell into account. Its reliability is assessed by monitoring the evolution of the spread and average of the observables of interest.

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Section: Introductionmentioning

confidence: 99%

“…However, as the configuration space can become very large, computational costs of carrying out first‐principles calculations grow rapidly. As a result, they cannot be performed for every intermediate in large reaction networks . This issue can be overcome by the application of a reactive force‐field .…”

Section: Introductionmentioning

confidence: 99%

Systematic microsolvation approach with a cluster‐continuum scheme and conformational sampling

2020

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“…With the computer technological advances, the calculation step can be less time consuming than the setup construction and the results analysis. Adapted from[145]. Copyright © 2018 American Chemical Society.…”

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confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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“…In recent years, various techniques for performing automated reaction path search on quantum chemical potential energy surface (PES) have been developed . The obtained multiple reaction paths form a network called reaction path network.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, various techniques for performing automated reaction path search on quantum chemical potential energy surface (PES) have been developed. [7][8][9] The obtained multiple reaction paths form a network called reaction path network. We have developed the artificial force induced reaction (AFIR) method for obtaining reaction path networks [10] and the rate constant matrix contraction (RCMC) method for analyzing them.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Acetalization Reaction Based on its Reaction Path Network

2019

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We have theoretically investigated the mechanism of the acetalization reaction of acetaldehyde using the artificial force induced reaction (AFIR) method and the rate constant matrix contraction (RCMC) method. A systematic search for reaction paths by the AFIR method resulted in a reaction path network including 162 local minima and 599 transition states. By applying the RCMC method to the network, the time hierarchy of the network was revealed. Using the network and the RCMC method, the overall rate constants and the equilibrium constant including conformational entropy contributions were evaluated. The discussion demonstrates how multicomponent reactions that proceed through multiple steps are understood from their reaction path networks.

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Exploration of Reaction Pathways and Chemical Transformation Networks

Cited by 197 publications

References 261 publications

Systematic microsolvation approach with a cluster‐continuum scheme and conformational sampling

Systematic microsolvation approach with a cluster‐continuum scheme and conformational sampling

From DFT to machine learning: recent approaches to materials science–a review

Understanding the Acetalization Reaction Based on its Reaction Path Network

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