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

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

You might also read

Related Articles

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

Sort by
Same author

CoRE 2D-HOIP DB: Computation-Ready, Experimental Database of Two-Dimensional Hybrid Organic-Inorganic Perovskites.

Journal of chemical information and modeling·2026
Same author

gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction.

Journal of chemical information and modeling·2025
Same author

GENA-LM: a family of open-source foundational DNA language models for long sequences.

Nucleic acids research·2025
Same author

The carbon footprint of predicting CO<sub>2</sub> storage capacity in metal-organic frameworks within neural networks.

iScience·2024
Same author

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design.

Journal of chemical information and modeling·2024
Same author

Accurate, interpretable predictions of materials properties within transformer language models.

Patterns (New York, N.Y.)·2023

Related Experiment Video

Updated: May 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Enhancing composition-based materials property prediction by cross-modal knowledge transfer.

Ivan Rubtsov1,2, Ivan Dudakov1,2, Yuri Kuratov2,3

  • 1AI Center, Lomonosov Moscow State University, Moscow, Russia, 119991.

Scientific Reports
|May 20, 2026
PubMed
Summary

This study introduces a universal approach for materials property prediction using cross-modal knowledge transfer in chemical language models. This method enhances accuracy and explores new chemical spaces, achieving state-of-the-art results.

Keywords:
chemical language modelsmaterials property predictionmultimodal learningtransformers

Related Experiment Videos

Last Updated: May 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Crystal graph neural networks excel at modeling known materials.
  • Structure-agnostic algorithms explore uncharted chemical spaces.
  • Predicting material properties from composition alone is challenging.

Purpose of the Study:

  • To develop a universal approach for enhancing composition-based materials property prediction.
  • To leverage cross-modal knowledge transfer in chemical language models.
  • To improve the exploration of chemical space for materials discovery.

Main Methods:

  • Proposed two formulations: implicit transfer (pretraining on multimodal embeddings) and explicit transfer (generating crystal structures for structure-aware predictors).
  • Utilized chemical language models and cross-modal knowledge transfer.
  • Applied a game-theoretic approach to enhance model interpretability.

Main Results:

  • Achieved state-of-the-art performance in 25 out of 32 benchmark tasks (LLM4Mat-Bench and MatBench).
  • Demonstrated the effectiveness of both implicit and explicit cross-modal transfer.
  • Showcased improved interpretability of chemical language models.

Conclusions:

  • The proposed universal approach significantly enhances materials property prediction accuracy.
  • Cross-modal knowledge transfer offers a powerful strategy for exploring chemical space.
  • The methods advance the capabilities of AI in materials science and discovery.