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Prochirality02:05

Prochirality

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The concept of prochirality leads to the nomenclature of the individual faces of a molecule and plays a crucial role in the enantioselective reaction. It is a concept where two or more achiral molecules react to produce chiral products. A typical process is the reaction of an achiral ketone to generate a chiral alcohol. Here, the achiral reactant reacts with an achiral reducing agent, sodium borohydride, to generate an equimolar mixture of the chiral enantiomers of the product. For example, an...
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Sulfides are the sulfur analog of ethers, just as thiols are the sulfur analog of alcohol. Like ethers, sulfides also consist of two hydrocarbon groups bonded to the central sulfur atom. Depending upon the type of groups present, sulfides can be symmetrical or asymmetrical. Symmetrical sulfides can be prepared via an SN2 reaction between 2 equivalents of an alkyl halide and one equivalent of sodium sulfide.
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Chirality is most prevalent in carbon-based tetrahedral compounds, but this important facet of molecular symmetry extends to sp3-hybridized nitrogen, phosphorus and sulfur centers, including trivalent molecules with lone pairs. Here, the lone pair behaves as a functional group in addition to the other three substituents to form an analogous tetrahedral center that can be chiral.
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Molecules that possess multiple chiral centers can afford a large number of stereoisomers. For instance, while some molecules like 2-butanol have one chiral center, defined as a tetrahedral carbon atom with four different substituents attached, several molecules like butane-2,3-diol have multiple chiral centers. A simple formula to predict the number of stereoisomers possible for a molecule with n chiral centers is 2n. However, there can be a lower number where some of the stereoisomers are...
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Machine Learning Guided Synthesis of Multinary Chevrel Phase Chalcogenides.

Nicholas R Singstock1, Jessica C Ortiz-Rodríguez2, Joseph T Perryman2

  • 1Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80303, United States.

Journal of the American Chemical Society
|June 10, 2021
PubMed
Summary

Machine learning accelerates the discovery of novel Chevrel phase (CP) materials for energy applications. A new descriptor, Hδ, predicts synthesizable CPs, leading to the identification and successful synthesis of new telluride compounds.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid-State Chemistry

Background:

  • Chevrel phases (CPs) are promising molybdenum chalcogenides for energy applications but are underexplored due to synthesis challenges.
  • Limited synthesis data hinders the discovery of new CP materials.

Purpose of the Study:

  • To develop a machine-learned descriptor (Hδ) for predicting the stability and synthesizability of Chevrel phases.
  • To accelerate the discovery of novel CP materials, particularly tellurides.

Main Methods:

  • Density Functional Theory (DFT) calculations for decomposition enthalpy (ΔHd).
  • Machine learning (SIFT) to generate >560,000 descriptors for 438 CP compositions.
  • Screening >200,000 compositions using Hδ to identify synthesizable CPs (ΔHd < 65 meV/atom).

Main Results:

  • Identified 48,501 potentially synthesizable CPs, including 2,307 CP tellurides.
  • Successfully synthesized 5 out of 5 novel CP tellurides predicted to be stable.
  • Confirmed predicted channel site occupation preference in synthesized telluride CPs.

Conclusions:

  • The interpretable Hδ descriptor accurately predicts CP stability and guides material discovery.
  • The computational-experimental approach accelerates the identification of novel materials in sparse chemical spaces.
  • This methodology is transferable to accelerate the discovery of other material families.