Structural phase transition of monochalcogenides investigated with machine learning

As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency.