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Hydrogen Bonds00:26

Hydrogen Bonds

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Hydrogen bonds are weak attractions between atoms that have formed other chemical bonds. One of these atoms is electronegative, like oxygen, and has a partial negative charge. The other is a hydrogen atom that has bonded with another electronegative atom and has a partial positive charge.
Hydrogen Bonds Control the World!
Because hydrogen has very weak electronegativity when it binds with a strongly electronegative atom, such as oxygen or nitrogen, electrons in the bond are unequally shared....
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Hydrogen Bonds01:04

Hydrogen Bonds

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A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

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Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
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Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

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Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the...
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Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
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Seeking metal-organic frameworks for hydrogen storage using classical and quantum active learning.

Maicon Pierre Lourenço1, Rishabh Shukla2, Mosayeb Naseri2,3

  • 1Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, 29500-000 Alegre, Espírito Santo, Brazil. maiconpl01@gmail.com.

Physical Chemistry Chemical Physics : PCCP
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Summary

Artificial intelligence, including quantum active learning (QAL), can efficiently discover metal-organic frameworks (MOFs) for optimal hydrogen storage with minimal data. These methods identify promising MOFs and experimental conditions for enhanced gas adsorption.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Metal-organic frameworks (MOFs) are advanced porous materials with significant potential for gas storage and separation.
  • Efficient hydrogen storage is crucial for clean energy applications, and MOFs offer a promising avenue.
  • Discovering optimal MOFs and experimental conditions for hydrogen storage is challenging due to the vast design space.

Purpose of the Study:

  • To develop and evaluate active learning (AL) and quantum active learning (QAL) methods for designing MOFs with enhanced hydrogen storage capabilities.
  • To explore the performance of AL and QAL in identifying optimal MOFs and experimental parameters (temperature, pressure) using a literature-derived dataset.
  • To assess the ability of these AI techniques to efficiently navigate the MOF design space with limited experimental data.

Main Methods:

  • Implementation of AL and QAL methodologies for MOF discovery.
  • Utilized regression models including artificial neural networks, support vector regression, and Gaussian processes (GP and QGP).
  • Employed various uncertainty quantification and acquisition functions for selecting next experimental targets; QAL specifically used QGP with a projected quantum kernel.

Main Results:

  • AL and QAL methods successfully identified MOFs with enhanced hydrogen storage properties using a limited dataset.
  • The AI approaches demonstrated the ability to distinguish optimal MOFs and conditions from similar candidates.
  • A network graph method was developed to analyze the performance of AL and QAL in the MOF search.

Conclusions:

  • Active learning and quantum active learning are effective tools for accelerating the discovery of MOFs with superior hydrogen storage capacity.
  • These AI-driven methods can significantly reduce the experimental effort required for identifying new materials and optimal conditions.
  • The study highlights the potential of classical and quantum computing approaches for advancing materials design in energy applications.