2023-Sustainable Industrial Processing Summit
SIPS2023 Volume 9. Navrotsky Intl. Symp. / Geochemistry
| Editors: | F. Kongoli, M. Asta, C. Drouet, S.Hayun, R. Riedel, W. Sun |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2023 |
| Pages: | 142 pages |
| ISBN: | 978-1-989820-88-9 (CD) |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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DEEP LEARNING FOR LARGE-SCALE PREDICTION OF MELTING TEMPERATURE AND MATERIALS PROPERTIES
Qijun Hong1;1ARIZONA STATE UNIVERSITY, Tempe, United States;
Type of Paper: Regular
Id Paper: 319
Topic: 67
Abstract:
High-temperature materials properties are challenging to compute from first principles or machine learning. We demonstrate the feasibility of rapid and large-scale prediction of melting temperature prediction based on integrated density functional theory calculations and deep learning. We employ the method and tool to discover the material with the highest melting temperature in the world, as well as dozens of potential candidates of refractory materials.
We build cyber infrastructure to provide public access to our databases and models. Our application programming interface (API) enables users to swiftly calculate the melting temperatures of as many as 10,000 materials in a single API call, with a processing speed of 0.03 seconds per material. This valuable functionality is accessible to the general public free of charge, with all calculations being handled by our server hosted at the ASU Research Computing Center. An alternative way for users to access our models is by visiting our website through a browser, where they can perform single material calculations.
In order to extend the applicability of our methodology, we have constructed deep learning models for the prediction of various material properties beyond melting temperature, such as bulk modulus, volume, fusion enthalpy, and others. We build the Materials Properties Prediction (MAPP) framework, characterized by a diverse array of materials properties, the potential for iterative enhancement, and the prospect of model integration for systematic improvement.
Keywords:
Thermodynamics; Machine learning; Density functional theoryReferences:
[1] Hong, Q.-J, S.V. Ushakov, A. Navrotsky, and A. van de Walle, "Melting temperature prediction using a graph neural network model: from ancient minerals to new materials,", PNAS, 119.36 (2022): e2209630119.[2] Hong, Q.-J, "Melting temperature prediction via first principles and deep learning,", Computational Materials Science, 214 (2022): 111684.
[3] Hong, Q.-J, A. van de Walle, S.V. Ushakov, and A. Navrotsky, "Integrating computational and experimental thermodynamics of refractory materials at high temperature,", CALPHAD, 79 (2022): 102500.