论文标题
通过实验驱动的自动化机器学到了难治性氧化物的LNTER-ATOMIC潜力
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
论文作者
论文摘要
了解难治性氧化物的结构和特性对于高温应用至关重要。在这项工作中，合并的实验和仿真方法使用主动学习者使用自动闭环，该闭合环由X射线和中子衍射测量初始化，并顺序改进机器学习模型，直到涵盖了实验预定的相位空间。通过在〜2900oC处绘制从室温到液态的最少数量的训练配置，为原型难治氧化物HFO2的规范示例生成了多相电位。该方法大大减少了模型的开发时间和人为努力。
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses an automated closed loop via an active-learner, which is initialized by X-ray and neutron diffraction measurements, and sequentially improves a machine-learning model until the experimentally predetermined phase space is covered. A multi-phase potential is generated for a canonical example of the archetypal refractory oxide, HfO2, by drawing a minimum number of training configurations from room temperature to the liquid state at ~2900oC. The method significantly reduces model development time and human effort.
