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Predicting Synthesizability of Crystalline Materials via Deep Learning

Engineering and Technology

Predicting Synthesizability of Crystalline Materials via Deep Learning

A. Davariashtiyani, Z. Kadkhodaie, et al.

Discover how a deep-learning model leverages three-dimensional images of crystal structures to predict the synthesizability of hypothetical crystals. This groundbreaking research, conducted by Ali Davariashtiyani, Zahra Kadkhodaie, and Sara Kadkhodaei, showcases an innovative approach to identifying viable materials for battery electrodes and thermoelectric applications.

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Playback language: English
Abstract
Predicting the synthesizability of hypothetical crystals is challenging due to the wide range of parameters governing materials synthesis. This paper presents a deep-learning model that uses three-dimensional pixel-wise images of crystal structures, color-coded by chemical attributes, to learn features of synthesizability. A convolutional encoder learns these features, enabling accurate classification of materials into synthesizable crystals versus crystal anomalies. The model's usefulness is demonstrated by predicting synthesizability for hypothetical crystals for battery electrode and thermoelectric applications.
Publisher
Communications Materials
Published On
Nov 18, 2021
Authors
Ali Davariashtiyani, Zahra Kadkhodaie, Sara Kadkhodaei
Tags
synthesizability
deep learning
crystal structures
convolutional encoder
battery electrodes
thermoelectric materials
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