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Related Concept Videos

Structural Isomerism02:34

Structural Isomerism

Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can be...
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...
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
Bending of Members Made of Several Materials01:11

Bending of Members Made of Several Materials

In analyzing a structural member composed of two different materials with identical cross-sectional areas, it is crucial to understand how their distinct elastic properties affect the member's response under load. The analysis involves assessing stress and strain distributions using the transformed section concept, which accounts for variations in material properties.
Hooke's Law determines stress in each material, stating that stress is proportional to strain but varies due to each material's...
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 Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

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Updated: Jul 12, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials.

Seongmin Kim1, Seehyuk Kwon1, Jaehwan Choi1

  • 1Department of Chemical and Biological Engineering (BK21 Four), and Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.

Journal of Chemical Information and Modeling
|July 9, 2026
PubMed
Summary

Current computational models for material synthesizability show bias towards structurally similar data. Incorporating diverse material properties improves predictions for novel inorganic materials, bridging the gap between theory and experiment.

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Last Updated: Jul 12, 2026

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
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05:57

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

Published on: April 26, 2024

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning in Materials Discovery

Background:

  • A persistent challenge in computational materials design is the discrepancy between predicted properties and experimental synthesizability.
  • Data-driven semisupervised models offer an alternative to traditional thermodynamic stability estimates for predicting synthesizability.
  • The limitations and biases of these data-driven models, particularly concerning structural similarity, are not fully understood.

Purpose of the Study:

  • To systematically investigate the performance boundaries of semisupervised models for inorganic synthesizability prediction.
  • To quantify the impact of data structural similarity on model effectiveness.
  • To explore methods for mitigating biases and improving predictions for structurally dissimilar materials.

Main Methods:

  • Evaluated model performance under data manipulations: random, structurally similar, and structurally dissimilar case removals.
  • Compared performance shifts with thermodynamic-based synthesizability estimations.
  • Assessed the impact of incorporating complementary material properties beyond structural information.

Main Results:

  • Current semisupervised models exhibit a significant dependency on the structural similarity of training data.
  • Model effectiveness, especially for novel materials, is strongly influenced by this similarity bias.
  • Thermodynamic-based methods show different limitations compared to data-driven approaches.

Conclusions:

  • Structural similarity is a critical factor affecting the generalizability of current computational synthesizability prediction models.
  • Integrating diverse material properties alongside structural data can partially overcome this bias.
  • This approach enhances recall for structurally dissimilar materials, paving the way for more reliable inorganic material discovery.