Data driven materials discovery and optimization require databases that are error free and experimentally verified. Performing material measurements is 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 (Ba_xSr_1−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.

中文翻译：

---

交互式人机学习框架，用于铁电-介电复合材料建模

数据驱动的材料发现和优化需要无错误且经过实验验证的数据库。进行材料测量非常耗时，而且通常受到材料样品制备不费力，劳动强度大和价格昂贵这一事实的限制。为了解决这些问题，多年来对材料的数值建模进行了研究，如今，它已在多尺度和多物理学的层面上得到发展。但是，由于多种未知因素，包括系统中存在的氧空位密度，晶粒尺寸和畴界边界，纳米复合材料（尤其是铁电体）的数值模型受到限制。在这项工作中，我们通过开发可扩展的半经验模型来准确预测由深度学习（DL）启用的材料属性，从而引入人机交互学习框架。_选择x Sr _{1- x} TiO ₃）作为示例铁电-介电复合材料进行验证。DL模型从测量数据库中转移学习材料的实验特性，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而收集的100多种不同铁电复合材料的数据。训练有素的DL模型可用于向人类研究人员提供反馈，然后他们会相应地完善计算机模型参数，从而完成交互式学习周期。最后，已开发的DL模型可用于预测和优化具有最高品质因数（FOM）值的新型铁电介质复合材料。