Melting temperature prediction using a graph neural network model: From ancient minerals to new materials

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Abstract

The melting point is a fundamental property that is time-consuming to measure or compute, thus hindering high-throughput analyses of melting relations and phase diagrams over large sets of candidate compounds. To address this, we build a machine learning model, trained on a database of ∼10,000 compounds, that can predict the melting temperature in a fraction of a second. The model, made publicly available online, features graph neural network and residual neural network architectures. We demonstrate the model’s usefulness in diverse applications. For the purpose of materials design and discovery, we show that it can quickly discover novel multicomponent materials with high melting points. These predictions are confirmed by density functional theory calculations and experimentally validated. In an application to planetary science and geology, we employ the model to analyze the melting temperatures of ∼4,800 minerals to uncover correlations relevant to the study of mineral evolution.

Original languageEnglish (US)
Article numbere2209630119
JournalProceedings of the National Academy of Sciences of the United States of America
Volume119
Issue number36
DOIs
StatePublished - Sep 6 2022

Keywords

  • machine learning
  • melting temperature
  • mineral evolution

ASJC Scopus subject areas

  • General

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