Utilizing AI and TD-DFT Calculated Raman Response to Optimize CVD Synthesis Parameters of K₂CoS₂

With new materials being theoretically devised using computational techniques, such as Density Functional Theory (DFT), new experimental tactics must be devised to ease the complexities of synthesizing new 2D materials. Given the large parameter control space for experimental synthesis of 2D materials, we propose utilizing an AI model that correlates experimental growth parameters to their resultant sample Raman spectra measurements. Time-Dependent DFT (TD-DFT) is utilized to calculate an approximation for the expected Raman spectra of the desired 2D material using a wavelength-dependent Placzek approximative method [1], allowing the AI model to propose expected growth parameters.<br/>In this presentation we will discuss our preliminary results of ultrathin, possibly monolayer growth of K₂CoS₂, an in-plane antiferromagnetic insulator [2], using these TD-DFT simulations and AI to optimize CVD growth parameters.<br/><br/><b>References:</b><br/><br/>[1] M. Walter, M. Moseler, "Ab Initio Wavelength-Dependent Raman Spectra: Placzek Approximation and Beyond" J. Chem. Theory Comput., 2020, 16, 1, 576-586<br/>[2] A. B. Sarkar, A. Bansil, A, Agarwal et al., "K₂CoS₂: A two-dimensional in-plane antiferromagnetic insulator" Phys. Rev. B 102, 035420, 2020