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arXiv 2003.12465cond-mat.mtrl-sciphysics.app-phphysics.comp-ph
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铁电-介电复合材料交互式人机学习建模框架

Interactive Human-Machine Learning Framework for Modelling of Ferroelectric-Dielectric Composites

Ning Liu, Achintha Ihalage, Hangfeng Zhang, Henry Giddens, Haixue Yan, Yang Hao

摘要:

数据驱动的材料发现和优化需要无错误和实验验证的数据库。执行材料测量是耗时的,并且通常受到材料样品制备是不平凡的、劳动密集型的和昂贵的事实的限制。为了解决这些问题,多年来一直在研究材料的数值模拟,现在它已经在多尺度和多物理水平上发展。然而,纳米复合材料,特别是铁电体的数值模型是有限的,由于多个未知数,包括氧空位密度,晶粒尺寸和畴界存在于系统中。在这项工作中,我们通过开发一个可扩展的半经验模型来引入人机交互学习框架,以准确预测深度学习(DL)实现的材料特性。MgO掺杂的BST(Sr_2O_3)被选择作为用于验证的示例铁电-介电复合材料。DL模型从测量数据库中转移学习材料的实验特征,该数据库包括通过筛选已发布的数据并结合我们自己的测量数据收集的100多种不同铁电复合材料的数据。训练的DL模型用于向人类研究人员提供反馈,然后人类研究人员相应地改进计算机模型参数,从而完成交互式学习周期。最后,开发的DL模型应用于预测和优化新的铁电-介电复合材料的最高品质因数(FOM)值。

英文摘要:

Data driven materials discovery and optimization requires databases that are error free and experimentally verified. Performing material measurements are time-consuming and often restricted by the fact that material sample preparations are non-trivial, labour-intensive and expensive. Numerical modelling of materials has been studied over the years in order to address these issues and nowadays it has been developed at multi-scale and multi-physics levels. However, numerical models for nano-composites, especially for ferroelectrics are limited due to multiple unknowns including oxygen vacancy densities, grain sizes and domain boundaries existing in the system. In this work, we introduce a human-machine interactive learning framework by developing a scalable semi-empirical model to accurately predict material properties enabled by deep learning (DL). MgO-doped BST (BaxSr1-xTiO3) is selected as an example ferroelectric-dielectric composite for validation. The DL model transfer-learns the experimental features of materials from a measurement database which includes data for over 100 different ferroelectric composites collected by screening the published data and combining our own measurement data. The trained DL model is utilized in providing feedback to human researchers, who then refine computer model parameters accordingly, hence completing the interactive learning cycle. Finally, the developed DL model is applied to predict and optimise new ferroelectric-dielectric composites with the highest figure of merit (FOM) value.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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