ECE Department, Northwestern University, Evanston, Illinois 60208, United States.
Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.
J Chem Inf Model. 2023 Apr 10;63(7):1865-1871. doi: 10.1021/acs.jcim.3c00307. Epub 2023 Mar 27.
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor.
由于人工智能、机器学习和深度学习技术在材料科学领域具有从可用数据中提取和利用数据驱动信息的有前途的能力，并为未来的应用加速材料发现和设计，因此它们的应用变得越来越普遍。为了帮助实现这一目标，我们根据材料的组成，为多种材料特性部署了预测模型。这里描述的深度学习模型是使用跨特性深度迁移学习技术构建的，该技术利用在大型数据集上训练的源模型在具有不同特性的小型数据集上构建目标模型。我们将这些模型部署在一个在线软件工具中，该工具接受多个材料成分作为输入，执行预处理以生成每个材料的基于成分的属性，并将它们输入到预测模型中，以获得多达 41 种不同的材料特性值。材料特性预测器可在线获得，网址为 http://ai.eecs.northwestern.edu/MPpredictor。
