Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hydrogen Bonds00:26

Hydrogen Bonds

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.
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR01:15

¹H NMR of Conformationally Flexible Molecules: Variable-Temperature NMR

The axial and equatorial protons in cyclohexane can be distinguished by performing a variable-temperature NMR experiment. In this process, except for one proton, the remaining eleven protons are replaced by deuterium. The deuterium substitution avoids the possible peak splitting caused by the spin-spin coupling between the adjacent protons. The remaining proton flips between the axial and equatorial positions.
Hydrogen Bonds01:04

Hydrogen Bonds

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...
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Physics-Informed Neural Network Framework Integrating Soft and Hard Constraints for Predicting Biomass Gasification Syngas Compositions.

ACS omega·2026
Same author

Data-Driven Identification of Rational Nonlinear Dynamics in Biochemical Networks via an Implicit Singular Value Decomposition Based Framework.

IET systems biology·2026
Same author

Multiobjective Optimization of Metal-Organic Framework Structural Properties and Synthesis Costs through Machine Learning.

Journal of chemical information and modeling·2025
Same author

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

IEEE transactions on neural networks and learning systems·2023

Related Experiment Video

Updated: May 12, 2026

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia
12:05

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia

Published on: October 10, 2013

15.4K

Physics-Informed Machine Learning for Fast Screening High Hydrogen Storage MOFs with Monotonicity Constraints.

Xuanjie Chen1, Chunjian Pan1, Shaojun Ren2

  • 1College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, P.R. China.

Journal of Chemical Theory and Computation
|May 7, 2025
PubMed
Summary

Physics-Informed Neural Networks (PINNs) offer a physically consistent approach to screening Metal-Organic Frameworks (MOFs) for efficient hydrogen storage. This method successfully identified MOFs meeting U.S. Department of Energy targets for a carbon-neutral economy.

More Related Videos

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

48.0K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

3.4K

Related Experiment Videos

Last Updated: May 12, 2026

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia
12:05

Preparation of Hydrophobic Metal-Organic Frameworks via Plasma Enhanced Chemical Vapor Deposition of Perfluoroalkanes for the Removal of Ammonia

Published on: October 10, 2013

15.4K
Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

48.0K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

3.4K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Energy Storage

Background:

  • Hydrogen is a key alternative energy carrier for a carbon-neutral future.
  • Current hydrogen storage methods face efficiency and cost challenges.
  • Metal-Organic Frameworks (MOFs) show promise for hydrogen storage due to high surface area and tunability.

Purpose of the Study:

  • To develop a physically consistent machine learning model for screening MOFs for hydrogen storage.
  • To identify high-capacity MOFs that meet specific hydrogen storage targets.
  • To overcome limitations of traditional machine learning models lacking physical consistency.

Main Methods:

  • Development of a Physics-Informed Neural Network (PINN) incorporating crystallographic-property relationships.
  • Integration of monotonic relationships into the neural network architecture.
  • Validation of top-performing MOFs using Grand Canonical Monte Carlo (GCMC) simulations.

Main Results:

  • PINN successfully screened MOFs for high hydrogen storage capacity.
  • Identified MOFs exhibited meaningful crystallographic features via heatmap analysis.
  • Two experimentally synthesized MOFs, LADQEM_CSD17 and LADQEM01_CSD17, met U.S. DOE 2025 onboard hydrogen storage targets.

Conclusions:

  • PINNs provide a physically consistent and effective method for MOF screening in hydrogen storage.
  • The identified MOFs represent viable candidates for advanced hydrogen energy systems.
  • This approach accelerates the discovery of materials for efficient and cost-effective hydrogen storage.