Roadmap on Machine learning in electronic structure
Roadmap on Machine learning in electronic structure
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DOI：
10.1088/2516-1075/ac572f
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发表时间：
2022-06-01
影响因子：
2.6
通讯作者：
Ghiringhelli, L. M.
中科院分区：
文献类型：
--
作者：
Kulik, H. J.;Hammerschmidt, T.;Ghiringhelli, L. M.
近年来，我们见证了计算材料科学的范式转变。事实上，大多数在二十世纪下半叶发展起来的传统方法正在被更快、更简单且通常更准确的方法所补充、扩展，有时甚至完全取代。我们统称为机器学习的新方法起源于信息学和人工智能领域，但正在迅速侵入所有其他科学分支。考虑到这一点，这篇路线图文章由该领域专家的多项贡献组成，讨论了机器学习在材料科学中的使用，并分享了对材料特性预测、力场构建、密度泛函理论交换关联泛函的发展、多体问题解决等各种问题中当前和未来挑战的看法。尽管已经有无数令人兴奋的成功故事，但我们正处于一条漫长道路的开始，这条道路将重塑材料科学以应对二十一世纪的许多挑战。
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.