# From Random Networks to AI-Driven Glass Design

https://mdr.nims.go.jp/datasets/bd120803-fac3-4bbd-92d1-29ae89237870

## File

- [Abstracts_Krishnan.pdf](https://mdr.nims.go.jp/filesets/252ff106-187d-43b3-ba45-04ebd8eefabd/download) ([Detail](https://mdr.nims.go.jp/filesets/252ff106-187d-43b3-ba45-04ebd8eefabd.md))

## Id

bd120803-fac3-4bbd-92d1-29ae89237870

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## Visibility

open_to_public

## State

published

## Created at

2025-09-25T06:09:44.494531Z

## Updated at

2025-09-25T07:30:42.702215Z

## Published at

2025-09-25T07:20:21.198592Z

## Doi

https://doi.org/10.48505/nims.5780

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## Resource type

conference_presentation

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na

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## Title

- title: From Random Networks to AI-Driven Glass Design
  title_type: original
  lang: en

## Description

- description: "Artificial intelligence is transforming glass science from empirical
    trial-and-error approaches to predictive design tools, enabling unprecedented
    control over structure-property relationships. This talk presents how modern AI-driven
    methodologies revolutionize glass structure understanding, simulation, and discovery
    across experimental, computational, and theoretical domains. We demonstrate how
    computational modeling provides critical insights into glass structure and enables
    tailored design of novel materials. Through systematic examples spanning oxide,
    chalcogenide, and specialty glasses, we will discuss how physics-informed machine
    learning achieves remarkable accuracy in predicting mechanical, optical, and transport
    properties while enabling rational design of glasses with targeted functionalities.
    Our framework integrates several cutting-edge approaches: reinforcement learning
    coupled with differentiable simulations to optimize glass structures and directly
    predict properties from atomic configurations, comprehensive benchmarking of universal
    interatomic potentials revealing critical limitations and improvement pathways,
    and materials-domain language models that extract decades of accumulated glass
    science knowledge from literature. The integration of topological descriptors,
    network connectivity\r\nanalysis, and deep learning provides unprecedented insights
    into glass structure, capturing both short-range order and medium-range correlations
    that govern macroscopic behavior. This comprehensive approach accelerates glass
    discovery from decades to years or even months, enabling rational design of materials
    with tailored properties for applications ranging from nuclear waste immobilization
    to biomedical packaging, fundamentally transforming how we understand and design
    glassy materials."
  description_type: abstract
  lang: en

## Creator

- name: N. M. Anoop Krishnan
  role: author
  organization: Indian Institute of Technology Delhi
  department: Department of Civil Engineering

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## Publisher

organization: National Institute for Materials Science (NIMS)

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## Keyword

- subject: Machine learning
  schema: not_defined
- subject: AI-Driven Glass Design
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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- data_origin_type: other

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## Conference

name: The 17th International Conference on the Physics of Non-Crystalline Solids (PNCS17)
identifier: https://amorphous.tf.chiba-u.jp/pncs2025/

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## Fileset

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  filename: Abstracts_Krishnan.pdf
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## Thumbnail

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filename: Abstracts_Krishnan.pdf