Raman Spectroscopy of Transition Metal Dichalcogenide Mo_1-xW_xS_2-2ySe_2y Alloys through Machine-Learned Force Fields

Machine-learned Force Fields (MLFFs), developed using deep learning, harness the accuracy of Density Functional Theory (DFT) in conjunction with the computational efficiency and scalability of interatomic potentials. This synergy enables theoretical spectroscopy to delve into larger and more intricate systems than previously feasible with DFT alone. In this work, we develop a force field that learns the complex energy surface of quaternary Transition Metal Dichalcogenide (TMDC) alloy systems of the form Mo_1−xW_xS_2−2ySe_2y, using the equivariant Neural Network (NN) MACE[1]. We demonstrate the ability of this potential to calculate vibrational properties of alloy TMDCs including phonon spectra for pure monolayers, and VDOS and Raman spectra for alloys, retaining DFT-level accuracy while greatly extending feasible system size and degree of sampling over alloy configurations. We are able to characterize the Raman active modes across the whole range of concentration, particularly for the “disorder induced” modes. This potential can serve as a tool to aid experimentalists in studying and designing TMDC alloys for future applications.

1. [1] I. Batatia, D. P. Kovacs, G. N. C. Simm, C. Ortner, and G. Csanyi, MACE: Higher order equivariant message passing neural networks for fast and accurate force fields, Advances in Neural Information Processing Systems (2022)