Thekkepat Krishnamohan, Das Sumanjit, Prosad Dogra Debi, Gupta Kapil, Lee Seung-Cheol
Indo-Korea Science and Technology Center, Jakkur, Bangalore 560065, India.
Division of Nano & Information Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea.
J Phys Condens Matter. 2023 Sep 18;35(50). doi: 10.1088/1361-648X/acf637.
Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of CE Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50% reduction in the training set size. The resultant sparse models can sample the configuration space at least 3 × faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd and (Ge,Sn)(S,Se,Te).The sparse CE models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling.
多组分合金作为技术突破的驱动力正变得越来越重要，特别是在结构材料和储能材料方面。这些材料巨大的构型空间使得仅使用基于第一性原理的方法进行计算建模变得困难。簇展开（CE）方法是用于模拟合金中构型无序的最广泛使用的工具。CE依靠机器学习算法来训练哈密顿量，并将第一性原理计算的数据用作训练集。在本文中，我们提出了一种基于压缩感知的新算法，用于高效构建多组分合金的CE哈密顿量。我们的算法从精心挑选的少量合金结构训练集中构建高度稀疏且物理上合理的模型。与传统拟合算法相比，该算法使训练集大小减少了50%以上。由此产生的稀疏模型对构型空间的采样速度至少快3倍。我们在4种不同的合金体系上演示了该算法，即Ag-Au、Ag-Au-Cu、Ag-Au-Cu-Pd和(Ge,Sn)(S,Se,Te)。这些合金的稀疏CE模型可以快速重现已知的基态排序和有序-无序转变。我们的方法通过降低与模型构建和构型采样相关的成本，能够真正实现高通量多组分合金热力学。
