School for Engineering of Transport, Energy and Matter, Arizona State University, Tempe, AZ 85287.
Center for Materials of the University, School of Molecular Sciences, Arizona State University, Tempe, AZ 85287.
Proc Natl Acad Sci U S A. 2022 Sep 6;119(36):e2209630119. doi: 10.1073/pnas.2209630119. Epub 2022 Aug 31.
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.
熔点是一种基本性质，其测量或计算非常耗时，因此阻碍了对大量候选化合物的熔融关系和相图进行高通量分析。为了解决这个问题，我们构建了一个机器学习模型，该模型在大约 10000 种化合物的数据库上进行训练，可以在几分之一秒内预测熔点。该模型已在网上公开提供，具有图神经网络和残差神经网络架构。我们展示了该模型在各种应用中的有用性。对于材料设计和发现的目的，我们表明它可以快速发现具有高熔点的新型多组分材料。这些预测通过密度泛函理论计算和实验验证得到了证实。在行星科学和地质学的应用中，我们使用该模型分析了约 4800 种矿物质的熔点，以揭示与矿物质演化研究相关的相关性。
