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arXiv 2102.03006cond-mat.stat-mechcond-mat.dis-nnphysics.data-an
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正方形格子上各向异性粒子系统的机器学习相变

Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice

Karthik Padavala, Avaneesh Singh, Joyjit Kundu

摘要:

近年来,机器学习(ML)领域取得了惊人的增长。ML方法在物理学各个分支中的成功实施带来了新的见解。这些方法已被证明可以对凝聚态系统中的相进行分类。本文研究了正方形点阵上刚性硬杆系统在连续和不连续相变附近的相分类问题。在比较许多方法时,我们发现卷积神经网络(CNN)在仅给出快照作为输入时以最高的准确度对相位进行分类。我们研究了系统的大小如何影响模型的性能。我们进一步比较了CNN在对连续和不连续相变周围的相位进行分类方面的性能。此外,我们还表明,通过使用物理指导的特征,人们甚至可以用更简单的模型击败CNN的准确性。最后,我们表明,在这个系统中的临界点,可以学习没有任何事先估计,仅使用有序相位的信息(作为训练集)。我们的研究表明,在自旋系统研究中取得成功的ML技术可以很容易地适应更复杂的复杂系统。

英文摘要:

The area of Machine learning (ML) has seen exceptional growth in recent years. Successful implementation of ML methods in various branches of physics has led to new insights. These methods have been shown to classify phases in condensed matter systems. Here we study the classification problem of phases in a system of hard rigid rods on a square lattice around a continuous and a discontinuous phase transition. On comparing a number of methods we find that convolutional neural network (CNN) classifies the phases with the highest accuracy when only snapshots are given as inputs. We study how the system size affects the model performance. We further compare the performance of CNN in classifying the phases around a continuous and a discontinuous phase transition. Further, we show that one can even beat the accuracy of CNN with simpler models by using physics-guided features. Lastly, we show that the critical point in this system can be learned without any prior estimate by using only the information of the ordered phase (as training set). Our study reveals the ML techniques that have been successful in studying spin systems can be easily adapted to more complex systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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