Researchers have developed a deep learning-based approach, termed a2c, to predict the crystallization of amorphous materials into metastable and stable crystal forms. Crystallization from amorphous precursors, critical in fields like materials science and geology, typically leads to metastable phases. The a2c method utilizes advanced graph neural networks to analyze local structural motifs within amorphous configurations, identifying likely crystal structures with high accuracy. The approach accelerates predictions compared to traditional random searches, enabling applications across diverse inorganic systems. Validated through experimental data, a2c has been applied to complex systems like boron nitride and metallic glasses, offering insights into polymorph selection and phase transitions. This tool paves the way for innovative material synthesis, reducing reliance on trial-and-error methods and unlocking new pathways in the discovery of functional materials.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s43588-024-00752-y
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Many thanks for your interest and consideration,
Dr. Gabriele Mogni
Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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