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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
Author(s) Vineeth Venugopal, Sourav Sahoo, Mohd Zaki, Nitya Nand Gosvami, N. M. Anoop Krishnan
On-Site Speaker (Planned) N. M. Anoop Krishnan
Abstract Scope A large amount of information about materials is scattered in scientific journals, handbooks, patents, textbooks and other resources. The text and images comprise most of the information which is currently unstructured. To retrieve research papers related to particular topics in specialized materials science domains or get information from figure captions are trivial tasks. Therefore, to streamline information extraction from research papers, we present latent Dirichlet allocation (LDA) assisted topic labelling to obtain glass science papers on the basis of their abstract. Further, we develop “Caption Cluster Plots” (CCP) to automate information extraction from figure captions. Using both LDA and CCP, we have also developed “Elemental Maps” which disseminate the information about which chemical elements are used in abstracts of which research papers and associated figure captions. Hence, this pipeline will enable researchers to explore different material science domains and excavate the hidden information from the vast corpora of research articles.
Proceedings Inclusion? Undecided


A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

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