Deep-learning framework for fully-automated recognition of TiO₂ polymorphs based on Raman spectroscopy.

ABSTRACT: Emerging machine learning techniques can be applied to Raman spectroscopy measurements for the identification of minerals. In this project, we describe a deep learning-based solution for automatic identification of complex polymorph structures from their Raman signatures. We propose a new framework using Convolutional Neural Networks and Long Short-Term Memory networks for compound identification. We train and evaluate our model using the publicly-available RRUFF spectral database. For model validation purposes, we synthesized and identified different TiO₂ polymorphs to evaluate the performance and accuracy of the proposed framework. TiO₂ is a ubiquitous material playing a crucial role in many industrial applications. Its unique properties are currently used advantage

SUBMITTER: Bhattacharya A 

PROVIDER: S-EPMC9763332 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature