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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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Related Experiment Video

Updated: Jul 8, 2025

Fluid-cell Raman Spectroscopy for operando Studies of Reaction and Transport Phenomena during Silicate Glass Corrosion
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Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning.

Kumar Ayush1, Pooja Sahu2, Sk Musharaf Ali2

  • 1Department of Chemical Engineering and Center for Atomistic Modeling and Materials Design, Indian Institute of Technology Madras, Chennai, TN 600036, India. tpatra@iitm.ac.in.

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|December 15, 2023
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Summary

A new machine learning model accurately predicts the atomic structure of glasses from their composition. This approach accelerates the discovery and design of novel glass materials with tailored properties.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Glass Science

Background:

  • Glasses exhibit tunable thermophysical properties based on composition.
  • Establishing universal composition-property relationships in glasses is difficult due to vast compositional space.

Purpose of the Study:

  • To develop a machine learning (ML) metamodel for predicting the composition-atomistic structure relationship in glasses.
  • To create an automated pipeline for predicting glass atom spatial distribution.

Main Methods:

  • Integrated unsupervised deep learning (convolutional neural network autoencoder) with a regression algorithm (random forest).
  • Utilized molecular dynamics simulations to generate atomistic structures for silicate and sodium borosilicate glasses.
  • Developed a latent space representation for predicting pair correlation functions.

Main Results:

  • The ML model accurately predicts atom pair correlation functions for various glass compositions.
  • The automated pipeline successfully models the spatial distribution of atoms in glasses.
  • Validated the model's accuracy on unknown glass compositions.

Conclusions:

  • The developed ML framework provides a generic and accurate method for understanding glass atomistic structures.
  • This approach can significantly accelerate the design and discovery of new glasses.
  • The method offers fundamental insights into composition-structure-property relationships in glassy materials.