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如何有效搜索巨大的材料空间来获得目标性能是当前材料研究的主要挑战之一。计算机科学中的工具有望为解决上述问题提供新的方法和机遇。
来自洛斯阿拉莫斯、弗吉尼亚大学和西安交通大学的科学家综述了主动学习,这一计算机工具在加速材料研究和新材料发现方面的应用。该方法依赖于不确定性的使用,其首先基于近似模型来定义一个效用函数,通过最大化效用函数的值来确定下一步最佳的实验和计算体系。通过迭代循环的方式来扩大数据库并优化近似模型,最终可以得到具有目标性能的材料体系。该文综述了主动学习的计算框架,讨论了几种效用函数,并结合压电、光电和磷灰石材料设计等具体实例,展示主动学习在材料科学研究中的应用。本文最后展望了材料信息学这一新兴研究领域中未来的研究方向和面临的主要挑战。
该文近期发表于npj Computational Materials 5: 21 (2019),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
Turab Lookman, Prasanna V. Balachandran, Dezhen Xue & Ruihao Yuan
One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.
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