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meta-Directing Deactivators: –NO2, –CN, –CHO, –⁠CO2R, –COR, –CO2H01:13

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All meta-directing substituents are deactivating groups. These substituents withdraw electrons from the aromatic ring, making the ring less reactive toward electrophilic substitution. For example, the nitration of nitrobenzene is 100,000 times slower than that of benzene because of the deactivating effect of the nitro group. The first step in an electrophilic aromatic substitution is the addition of an electrophile to form a resonance-stabilized carbocation. The energy diagrams for...
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Related Experiment Video

Updated: Sep 27, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Geometry meta-optimization.

Daniel Huang1, Junwei Lucas Bao2, Jean-Baptiste Tristan3

  • 1Department of Computer Science, San Francisco State University, San Francisco, California 94132, USA.

The Journal of Chemical Physics
|April 9, 2022
PubMed
Summary
This summary is machine-generated.

Geometry meta-optimization using Gaussian process (GP) surrogates enhances efficiency by learning from past optimizations. This approach reduces computational cost for electronic structure calculations (ESCs) compared to standard surrogate model-based optimization.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Materials Science

Background:

  • Machine-learned surrogates, particularly Gaussian process (GP) surrogates, show promise in reducing computational expense for electronic structure calculations (ESCs).
  • Surrogate model-based (SMB) geometry optimization aims to minimize ESCs by using predictive models.
  • Existing methods require significant tuning and may not fully leverage accumulated optimization experience.

Purpose of the Study:

  • To investigate geometry meta-optimization using GP surrogates, where the optimizer learns from its past geometry optimization experiences.
  • To evaluate the effectiveness and tuning requirements of meta-optimization compared to standard SMB optimization.
  • To explore the application of geometry meta-optimization in conformational searches.

Main Methods:

  • Developed and tested a geometry meta-optimizer employing GP surrogates.
  • Validated the approach on the ANI-1 dataset for small organic molecules and a custom dataset of hydrocarbons and alcohols.
  • Compared performance against standard SMB optimization, focusing on ESC reduction and accuracy.

Main Results:

  • Geometry meta-optimization with GP surrogates demonstrated effectiveness and required less tuning than standard SMB optimization on the ANI-1 dataset.
  • GP surrogates preserving rotational invariance yielded greater ESC savings across multiple geometries.
  • Both SMB and meta-optimization methods saved ESCs but occasionally missed higher-energy conformers compared to traditional geometry optimization.

Conclusions:

  • Geometry meta-optimization with GP surrogates offers a more efficient and less tunable alternative for computational chemistry tasks.
  • Further research into the divergence between GP surrogates and potential energy surfaces is crucial for advancing surrogate-based methods.
  • This work highlights the potential of learned experience in optimizing machine learning models for chemical applications.