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

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